CANN: Add 310P operator support check (#12962)

* opencl: refactor - split the kernel files

---------

Co-authored-by: Shangqing Gu <quic_shawngu@quicinc.com>

* opencl: split more kernels into separate files

* opencl: specify subgroup size instead of querying it

* opencl: refine Adreno cl compiler version parsing

* opencl: skip some kernels not used by Adreno on old compilers

* opencl: refine logic for selecting Adreno kernels

* opencl: refine Adreno cl compiler version

* opencl: cleanup preprocessor for kernels

* opencl: consider Adreno CL compiler on Windows

* opencl: add final newline for `mul_mv_f16_f16.cl`

---------

Co-authored-by: Shangqing Gu <quic_shawngu@quicinc.com>

.github/workflows/build.yml

@@ -1766,16 +1766,17 @@ jobs:
     if: ${{ github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'Ascend NPU') }}
     defaults:
       run:
-       shell: bash -el {0}
-    runs-on: ubuntu-24.04-arm
+        shell: bash -el {0}
     strategy:
       matrix:
+        arch: [x86, aarch64]
         cann:
           - '8.1.RC1.alpha001-910b-openeuler22.03-py3.10'
         device:
           - 'ascend910b3'
         build:
           - 'Release'
+    runs-on: ${{ matrix.arch == 'aarch64' && 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
     container: ascendai/cann:${{ matrix.cann }}
     steps:
       - name: Checkout

.github/workflows/docker.yml

@@ -36,13 +36,13 @@ jobs:
       matrix:
         config:
           # Multi-stage build
-          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: false}
-          - { tag: "cuda", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
-          - { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: true}
-          - { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
-          - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
+          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, free_disk_space: false }
+          - { tag: "cuda", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false }
+          - { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true }
+          - { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false }
+          - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false }
           # Note: the rocm images are failing due to a compiler error and are disabled until this is fixed to allow the workflow to complete
-          #- {tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: true }
+          #- {tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, free_disk_space: true }
     steps:
       - name: Check out the repo
         uses: actions/checkout@v4

Makefile

@@ -780,10 +780,6 @@ ifdef GGML_HIP
 
 	MK_CPPFLAGS += -DGGML_USE_HIP -DGGML_USE_CUDA
 
-ifdef GGML_HIP_UMA
-	MK_CPPFLAGS += -DGGML_HIP_UMA
-endif # GGML_HIP_UMA
-
 	MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib
 	MK_LDFLAGS += -L$(ROCM_PATH)/lib64 -Wl,-rpath=$(ROCM_PATH)/lib64
 	MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas

README.md

@@ -97,6 +97,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [Flan T5](https://huggingface.co/models?search=flan-t5)
 - [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca)
 - [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b) + [GLMEdge-1.5b](https://huggingface.co/THUDM/glm-edge-1.5b-chat) + [GLMEdge-4b](https://huggingface.co/THUDM/glm-edge-4b-chat)
+- [x] [GLM-4-0414](https://huggingface.co/collections/THUDM/glm-4-0414-67f3cbcb34dd9d252707cb2e)
 - [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)
 - [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
 - [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a)
@@ -259,7 +260,9 @@ The [Hugging Face](https://huggingface.co) platform hosts a [number of LLMs](htt
 - [Trending](https://huggingface.co/models?library=gguf&sort=trending)
 - [LLaMA](https://huggingface.co/models?sort=trending&search=llama+gguf)
 
-You can either manually download the GGUF file or directly use any `llama.cpp`-compatible models from Hugging Face by using this CLI argument: `-hf <user>/<model>[:quant]`
+You can either manually download the GGUF file or directly use any `llama.cpp`-compatible models from [Hugging Face](https://huggingface.co/) or other model hosting sites, such as [ModelScope](https://modelscope.cn/), by using this CLI argument: `-hf <user>/<model>[:quant]`.
+
+By default, the CLI would download from Hugging Face, you can switch to other options with the environment variable `MODEL_ENDPOINT`. For example, you may opt to downloading model checkpoints from ModelScope or other model sharing communities by setting the environment variable, e.g. `MODEL_ENDPOINT=https://www.modelscope.cn/`.
 
 After downloading a model, use the CLI tools to run it locally - see below.
 

build-xcframework.sh

@@ -41,6 +41,11 @@ COMMON_CMAKE_ARGS=(
     -DGGML_OPENMP=${GGML_OPENMP}
 )
 
+XCODE_VERSION=$(xcodebuild -version 2>/dev/null | head -n1 | awk '{ print $2 }')
+MAJOR_VERSION=$(echo $XCODE_VERSION | cut -d. -f1)
+MINOR_VERSION=$(echo $XCODE_VERSION | cut -d. -f2)
+echo "Detected Xcode version: $XCODE_VERSION"
+
 check_required_tool() {
     local tool=$1
     local install_message=$2
@@ -325,21 +330,28 @@ combine_static_libraries() {
 
     # Platform-specific post-processing for device builds
     if [[ "$is_simulator" == "false" ]]; then
-        if command -v vtool &>/dev/null; then
+        if command -v xcrun vtool &>/dev/null; then
             case "$platform" in
                 "ios")
                     echo "Marking binary as a framework binary for iOS..."
-                    vtool -set-build-version ios ${IOS_MIN_OS_VERSION} ${IOS_MIN_OS_VERSION} -replace \
+                    xcrun vtool -set-build-version ios ${IOS_MIN_OS_VERSION} ${IOS_MIN_OS_VERSION} -replace \
                         -output "${base_dir}/${output_lib}" "${base_dir}/${output_lib}"
                     ;;
                 "visionos")
                     echo "Marking binary as a framework binary for visionOS..."
-                    vtool -set-build-version xros ${VISIONOS_MIN_OS_VERSION} ${VISIONOS_MIN_OS_VERSION} -replace \
+                    if [[ "$MAJOR_VERSION" -gt 16 ]] || [[ "$MAJOR_VERSION" -eq 16 && "$MINOR_VERSION" -gt 2 ]]; then
+                        echo "Xcode version greater than 16.2, using visionOS."
+                        VISION_OS_BUILD_VERSION="visionos"
+                    else
+                        echo "Xcode version less than or equal to 16.2, using xros."
+                        VISION_OS_BUILD_VERSION="xros"
+                    fi
+                    xcrun vtool -set-build-version ${VISION_OS_BUILD_VERSION} ${VISIONOS_MIN_OS_VERSION} ${VISIONOS_MIN_OS_VERSION} -replace \
                         -output "${base_dir}/${output_lib}" "${base_dir}/${output_lib}"
                     ;;
                 "tvos")
                     echo "Marking binary as a framework binary for tvOS..."
-                    vtool -set-build-version tvos ${TVOS_MIN_OS_VERSION} ${TVOS_MIN_OS_VERSION} -replace \
+                    xcrun vtool -set-build-version tvos ${TVOS_MIN_OS_VERSION} ${TVOS_MIN_OS_VERSION} -replace \
                         -output "${base_dir}/${output_lib}" "${base_dir}/${output_lib}"
                     ;;
             esac

common/arg.cpp

@@ -228,12 +228,13 @@ static bool common_download_file_single(const std::string & url, const std::stri
     curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
     curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
 
+    http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp");
     // Check if hf-token or bearer-token was specified
     if (!bearer_token.empty()) {
         std::string auth_header = "Authorization: Bearer " + bearer_token;
         http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str());
-        curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
     }
+    curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
 
 #if defined(_WIN32)
     // CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of
@@ -544,7 +545,10 @@ static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_
     curl_ptr       curl(curl_easy_init(), &curl_easy_cleanup);
     curl_slist_ptr http_headers;
     std::string res_str;
-    std::string url = "https://huggingface.co/v2/" + hf_repo + "/manifests/" + tag;
+
+    std::string model_endpoint = get_model_endpoint();
+
+    std::string url = model_endpoint + "v2/" + hf_repo + "/manifests/" + tag;
     curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
     curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L);
     typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * ptr, size_t size, size_t nmemb, void * data);
@@ -659,13 +663,8 @@ static void common_params_handle_model(
                 }
             }
 
-            std::string hf_endpoint = "https://huggingface.co/";
-            const char * hf_endpoint_env = getenv("HF_ENDPOINT");
-            if (hf_endpoint_env) {
-                hf_endpoint = hf_endpoint_env;
-                if (hf_endpoint.back() != '/') hf_endpoint += '/';
-            }
-            model.url = hf_endpoint + model.hf_repo + "/resolve/main/" + model.hf_file;
+            std::string model_endpoint = get_model_endpoint();
+            model.url = model_endpoint + model.hf_repo + "/resolve/main/" + model.hf_file;
             // make sure model path is present (for caching purposes)
             if (model.path.empty()) {
                 // this is to avoid different repo having same file name, or same file name in different subdirs

common/chat.cpp

@@ -1622,7 +1622,7 @@ static common_chat_params common_chat_templates_apply_jinja(
     }
 
     // Hermes 2/3 Pro, Qwen 2.5 Instruct (w/ tools)
-    if (src.find("<tool_call>") != std::string::npos && params.json_schema.is_null()) {
+    if (src.find("<tool_call>") != std::string::npos && params.json_schema.is_null() && params.tools.is_array() && params.json_schema.is_null()) {
         return common_chat_params_init_hermes_2_pro(tmpl, params);
     }
 

common/common.cpp

@@ -830,7 +830,7 @@ std::string fs_get_cache_directory() {
     if (getenv("LLAMA_CACHE")) {
         cache_directory = std::getenv("LLAMA_CACHE");
     } else {
-#ifdef __linux__
+#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX)
         if (std::getenv("XDG_CACHE_HOME")) {
             cache_directory = std::getenv("XDG_CACHE_HOME");
         } else {
@@ -840,7 +840,9 @@ std::string fs_get_cache_directory() {
         cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
 #elif defined(_WIN32)
         cache_directory = std::getenv("LOCALAPPDATA");
-#endif // __linux__
+#else
+#  error Unknown architecture
+#endif
         cache_directory = ensure_trailing_slash(cache_directory);
         cache_directory += "llama.cpp";
     }
@@ -1027,6 +1029,19 @@ struct common_init_result common_init_from_params(common_params & params) {
     return iparams;
 }
 
+std::string get_model_endpoint() {
+    const char * model_endpoint_env = getenv("MODEL_ENDPOINT");
+    // We still respect the use of environment-variable "HF_ENDPOINT" for backward-compatibility.
+    const char * hf_endpoint_env = getenv("HF_ENDPOINT");
+    const char * endpoint_env = model_endpoint_env ? model_endpoint_env : hf_endpoint_env;
+    std::string model_endpoint = "https://huggingface.co/";
+    if (endpoint_env) {
+        model_endpoint = endpoint_env;
+        if (model_endpoint.back() != '/') model_endpoint += '/';
+    }
+    return model_endpoint;
+}
+
 void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora) {
     llama_clear_adapter_lora(ctx);
     for (auto & la : lora) {

common/common.h

@@ -543,6 +543,8 @@ struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_p
 // clear LoRA adapters from context, then apply new list of adapters
 void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora);
 
+std::string                   get_model_endpoint();
+
 //
 // Batch utils
 //

convert_hf_to_gguf.py

@@ -65,6 +65,7 @@ class Model:
     model_name: str | None
     metadata_override: Path | None
     dir_model_card: Path
+    remote_hf_model_id: str | None
 
     # subclasses should define this!
     model_arch: gguf.MODEL_ARCH
@@ -73,7 +74,7 @@ class Model:
                  use_temp_file: bool = False, eager: bool = False,
                  metadata_override: Path | None = None, model_name: str | None = None,
                  split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False,
-                 small_first_shard: bool = False, hparams: dict[str, Any] | None = None):
+                 small_first_shard: bool = False, hparams: dict[str, Any] | None = None, remote_hf_model_id: str | None = None):
         if type(self) is Model:
             raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")
 
@@ -83,11 +84,24 @@ class Model:
         self.is_big_endian = is_big_endian
         self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
         self.use_temp_file = use_temp_file
-        self.lazy = not eager
-        self.part_names = Model.get_model_part_names(self.dir_model, "model", ".safetensors")
-        self.is_safetensors = len(self.part_names) > 0
-        if not self.is_safetensors:
-            self.part_names = Model.get_model_part_names(self.dir_model, "pytorch_model", ".bin")
+        self.lazy = not eager or (remote_hf_model_id is not None)
+        self.remote_hf_model_id = remote_hf_model_id
+        if remote_hf_model_id is not None:
+            self.is_safetensors = True
+
+            def get_remote_tensors() -> Iterator[tuple[str, Tensor]]:
+                logger.info(f"Using remote model with HuggingFace id: {remote_hf_model_id}")
+                remote_tensors = gguf.utility.SafetensorRemote.get_list_tensors_hf_model(remote_hf_model_id)
+                self.tensor_names = set(name for name in remote_tensors.keys())
+                for name, remote_tensor in gguf.utility.SafetensorRemote.get_list_tensors_hf_model(remote_hf_model_id).items():
+                    yield (name, LazyTorchTensor.from_remote_tensor(remote_tensor))
+
+            self.get_tensors = get_remote_tensors
+        else:
+            self.part_names = Model.get_model_part_names(self.dir_model, "model", ".safetensors")
+            self.is_safetensors = len(self.part_names) > 0
+            if not self.is_safetensors:
+                self.part_names = Model.get_model_part_names(self.dir_model, "pytorch_model", ".bin")
         self.hparams = Model.load_hparams(self.dir_model) if hparams is None else hparams
         self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"])
         self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
@@ -393,6 +407,10 @@ class Model:
 
         self.metadata = gguf.Metadata.load(self.metadata_override, self.dir_model_card, self.model_name, total_params)
 
+        # If we are using HF model id, set the metadata name to the model id
+        if self.remote_hf_model_id:
+            self.metadata.name = self.remote_hf_model_id
+
         # Fallback to model directory name if metadata name is still missing
         if self.metadata.name is None:
             self.metadata.name = self.dir_model.name
@@ -717,6 +735,9 @@ class Model:
         if chkhsh == "d353350c764d8c3b39c763113960e4fb4919bea5fbf208a0e3b22e8469dc7406":
             # ref: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct
             res = "llama4"
+        if chkhsh == "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2":
+            # ref: https://huggingface.co/THUDM/glm-4-9b-hf
+            res = "glm4"
 
         if res is None:
             logger.warning("\n")
@@ -1732,7 +1753,7 @@ class LlamaModel(Model):
 
                 low_freq_wavelen = old_context_len / low_freq_factor
                 high_freq_wavelen = old_context_len / high_freq_factor
-                assert low_freq_wavelen != high_freq_wavelen
+                # assert low_freq_wavelen != high_freq_wavelen # Errors for Llama4
 
                 rope_factors = []
                 for freq in freqs:
@@ -1788,10 +1809,6 @@ class Llama4Model(LlamaModel):
         self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size_moe"])
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
-        name = name.replace("language_model.", "")
-        name = name.replace("feed_forward.", "mlp.") # a bit hacky for now
-        name = name.replace(".router.weight", ".gate.weight") # a bit hacky for now
-
         # split the gate_up into gate and up
         if "gate_up_proj" in name:
             name_up = name.replace("gate_up_proj", "up_proj.weight")
@@ -4405,6 +4422,10 @@ class DeepseekV2Model(Model):
         self._set_vocab_gpt2()
 
     def set_gguf_parameters(self):
+
+        # note: deepseek2 using MLA converts into MQA (ie: GQA with 1 group)
+        self.hparams["num_key_value_heads"] = 1
+
         super().set_gguf_parameters()
         hparams = self.hparams
 
@@ -4413,8 +4434,13 @@ class DeepseekV2Model(Model):
         if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None:
             self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
         self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"])
-        self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
-        self.gguf_writer.add_value_length(hparams["v_head_dim"])
+
+        # note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA
+        self.gguf_writer.add_key_length(hparams["kv_lora_rank"] + hparams["qk_rope_head_dim"])
+        self.gguf_writer.add_value_length(hparams["kv_lora_rank"])
+        self.gguf_writer.add_key_length_mla(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
+        self.gguf_writer.add_value_length_mla(hparams["v_head_dim"])
+
         self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
         self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
         self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
@@ -4483,6 +4509,26 @@ class DeepseekV2Model(Model):
             else:
                 return []
 
+        # note: MLA with the absorption optimization, needs these two split and k_b_proj transposed
+        if name.endswith("kv_b_proj.weight"):
+            name_kb = name.replace("kv_b_proj", "k_b_proj")
+            name_vb = name.replace("kv_b_proj", "v_b_proj")
+
+            n_head_kv = self.hparams["num_key_value_heads"]
+            v_head_dim = self.hparams["v_head_dim"]
+            qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
+
+            assert data_torch.shape[0] == n_head_kv * (v_head_dim + qk_nope_head_dim)
+
+            kv_b = data_torch.view(n_head_kv, v_head_dim + qk_nope_head_dim, data_torch.shape[-1])
+            k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1)
+            k_b = k_b.transpose(1, 2)
+
+            return [
+                (self.map_tensor_name(name_kb), k_b),
+                (self.map_tensor_name(name_vb), v_b)
+            ]
+
         return [(self.map_tensor_name(name), data_torch)]
 
     def prepare_tensors(self):
@@ -4883,6 +4929,22 @@ class JaisModel(Model):
         self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)
 
 
+@Model.register("Glm4ForCausalLM")
+class Glm4Model(Model):
+    model_arch = gguf.MODEL_ARCH.GLM4
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
+            if self.hparams["rope_scaling"].get("type") == "yarn":
+                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+                self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
+                self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"])
+
+
 @Model.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
 class ChatGLMModel(Model):
     model_arch = gguf.MODEL_ARCH.CHATGLM
@@ -5403,6 +5465,14 @@ class LazyTorchTensor(gguf.LazyBase):
         lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(st_slice,), func=lambda s: s[:])
         return cast(torch.Tensor, lazy)
 
+    @classmethod
+    def from_remote_tensor(cls, remote_tensor: gguf.utility.RemoteTensor):
+        dtype = cls._dtype_str_map[remote_tensor.dtype]
+        shape = remote_tensor.shape
+        meta = cls.meta_with_dtype_and_shape(dtype, shape)
+        lazy = cls(meta=meta, args=(remote_tensor,), func=lambda r: torch.frombuffer(r.data(), dtype=dtype).reshape(shape))
+        return cast(torch.Tensor, lazy)
+
     @classmethod
     def __torch_function__(cls, func, types, args=(), kwargs=None):
         del types  # unused
@@ -5480,6 +5550,10 @@ def parse_args() -> argparse.Namespace:
         "--print-supported-models", action="store_true",
         help="Print the supported models"
     )
+    parser.add_argument(
+        "--remote", action="store_true",
+        help="(Experimental) Read safetensors file remotely without downloading to disk. Config and tokenizer files will still be downloaded. To use this feature, you need to specify Hugging Face model repo name instead of a local directory. For example: 'HuggingFaceTB/SmolLM2-1.7B-Instruct'. Note: To access gated repo, set HF_TOKEN environment variable to your Hugging Face token.",
+    )
 
     args = parser.parse_args()
     if not args.print_supported_models and args.model is None:
@@ -5520,6 +5594,14 @@ def main() -> None:
 
     dir_model = args.model
 
+    if args.remote:
+        from huggingface_hub import snapshot_download
+        local_dir = snapshot_download(
+            repo_id=str(dir_model),
+            allow_patterns=["LICENSE", "*.json", "*.md", "*.txt", "tokenizer.model"])
+        dir_model = Path(local_dir)
+        logger.info(f"Downloaded config and tokenizer to {local_dir}")
+
     if not dir_model.is_dir():
         logger.error(f'Error: {args.model} is not a directory')
         sys.exit(1)
@@ -5541,6 +5623,9 @@ def main() -> None:
 
     if args.outfile is not None:
         fname_out = args.outfile
+    elif args.remote:
+        # if remote, use the model ID as the output file name
+        fname_out = Path("./" + str(args.model).replace("/", "-") + "-{ftype}.gguf")
     else:
         fname_out = dir_model
 
@@ -5551,7 +5636,6 @@ def main() -> None:
     with torch.inference_mode():
         output_type = ftype_map[args.outtype]
         model_architecture = hparams["architectures"][0]
-
         try:
             model_class = Model.from_model_architecture(model_architecture)
         except NotImplementedError:
@@ -5564,7 +5648,8 @@ def main() -> None:
                                      metadata_override=args.metadata, model_name=args.model_name,
                                      split_max_tensors=args.split_max_tensors,
                                      split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run,
-                                     small_first_shard=args.no_tensor_first_split)
+                                     small_first_shard=args.no_tensor_first_split,
+                                     remote_hf_model_id=str(args.model) if args.remote else None)
 
         if args.vocab_only:
             logger.info("Exporting model vocab...")

convert_hf_to_gguf_update.py

@@ -114,6 +114,7 @@ models = [
     {"name": "trillion",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/trillionlabs/Trillion-7B-preview", },
     {"name": "bailingmoe",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-lite", },
     {"name": "llama4",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct", },
+    {"name": "glm4",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", },
 ]
 
 

docs/build.md

@@ -259,8 +259,6 @@ You can download it from your Linux distro's package manager or from here: [ROCm
       cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
       && cmake --build build --config Release -- -j 16
   ```
-  On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DGGML_HIP_UMA=ON`.
-  However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).
 
   To enhance flash attention performance on RDNA3+ or CDNA architectures, you can utilize the rocWMMA library by enabling the `-DGGML_HIP_ROCWMMA_FATTN=ON` option. This requires rocWMMA headers to be installed on the build system.
 
@@ -296,6 +294,10 @@ You can download it from your Linux distro's package manager or from here: [ROCm
 The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used.
 If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3.
 
+### Unified Memory
+
+On Linux it is possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1`. However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).
+
 ## Vulkan
 
 **Windows**

examples/llava/CMakeLists.txt

@@ -1,3 +1,5 @@
+# llava (legacy)
+
 add_library(llava OBJECT
             llava.cpp
             llava.h
@@ -22,12 +24,41 @@ if (BUILD_SHARED_LIBS)
     install(TARGETS llava_shared LIBRARY)
 endif()
 
+# mtmd
+
+add_library(mtmd OBJECT
+            mtmd.cpp
+            mtmd.h
+            clip.cpp
+            clip.h
+            clip-impl.h
+            )
+
+target_link_libraries(mtmd PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})
+
+target_include_directories(mtmd PUBLIC .)
+target_include_directories(mtmd PRIVATE ../..)
+target_include_directories(mtmd PRIVATE ../../common) # for stb_image.h
+
+target_compile_features(mtmd PRIVATE cxx_std_17)
+
+add_library(mtmd_static STATIC $<TARGET_OBJECTS:mtmd>)
+if (BUILD_SHARED_LIBS)
+    set_target_properties(mtmd PROPERTIES POSITION_INDEPENDENT_CODE ON)
+    target_compile_definitions(mtmd PRIVATE LLAMA_SHARED LLAMA_BUILD)
+    add_library(mtmd_shared SHARED $<TARGET_OBJECTS:mtmd>)
+    target_link_libraries(mtmd_shared PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})
+    install(TARGETS mtmd_shared LIBRARY)
+endif()
+
 if (NOT MSVC)
     target_compile_options(llava PRIVATE -Wno-cast-qual) # stb_image.h
+    target_compile_options(mtmd PRIVATE -Wno-cast-qual) # stb_image.h
 endif()
 
 if(TARGET BUILD_INFO)
     add_dependencies(llava BUILD_INFO)
+    add_dependencies(mtmd BUILD_INFO)
 endif()
 
 set(TARGET llama-llava-cli)
@@ -55,7 +86,7 @@ set(TARGET llama-gemma3-cli)
 add_executable(${TARGET} gemma3-cli.cpp)
 set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-gemma3-cli)
 install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE common mtmd ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
 
 set(TARGET llama-llava-clip-quantize-cli)

examples/llava/clip-impl.h

@@ -1,5 +1,8 @@
 #include "ggml.h"
 #include "gguf.h"
+#include "clip.h"
+
+#include "clip.h"
 
 #include <climits>
 #include <cstdarg>
@@ -7,6 +10,7 @@
 #include <map>
 #include <sstream>
 #include <vector>
+#include <memory>
 
 // Internal header for clip.cpp
 
@@ -120,6 +124,23 @@ static projector_type clip_projector_type_from_string(const std::string & str) {
     return PROJECTOR_TYPE_UNKNOWN;
 }
 
+// RGB uint8 image
+struct clip_image_u8 {
+    int nx;
+    int ny;
+
+    std::vector<uint8_t> buf;
+};
+
+// RGB float32 image (NHWC)
+// Memory layout: RGBRGBRGB...
+struct clip_image_f32 {
+    int nx;
+    int ny;
+
+    std::vector<float> buf;
+};
+
 //
 // logging
 //
@@ -178,6 +199,36 @@ static void clip_log_internal(enum ggml_log_level level, const char * format, ..
 #define LOG_DBG(...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
 #define LOG_CNT(...) LOG_TMPL(GGML_LOG_LEVEL_CONT,  __VA_ARGS__)
 
+//
+// cpp wrappers
+//
+
+// wrapper for clip_image_size
+struct clip_image_size_deleter {
+    void operator()(clip_image_size * val) { clip_image_size_free(val); }
+};
+typedef std::unique_ptr<clip_image_size, clip_image_size_deleter> clip_image_size_ptr;
+
+// wrapper for clip_image_u8
+struct clip_image_u8_deleter {
+    void operator()(clip_image_u8 * val) { clip_image_u8_free(val); }
+};
+typedef std::unique_ptr<clip_image_u8, clip_image_u8_deleter> clip_image_u8_ptr;
+
+// wrapper for clip_image_f32
+struct clip_image_f32_deleter {
+    void operator()(clip_image_f32 * val) { clip_image_f32_free(val); }
+};
+typedef std::unique_ptr<clip_image_f32, clip_image_f32_deleter> clip_image_f32_ptr;
+
+struct clip_image_u8_batch {
+    std::vector<clip_image_u8_ptr> entries;
+};
+
+struct clip_image_f32_batch {
+    std::vector<clip_image_f32_ptr> entries;
+};
+
 //
 // common utils
 //
@@ -214,6 +265,20 @@ static void string_replace_all(std::string & s, const std::string & search, cons
     s = std::move(builder);
 }
 
+// split string by a `std::string delim` instead of `char delim`
+static std::vector<std::string> string_split_str(std::string s, const std::string & delimiter) {
+    std::vector<std::string> tokens;
+    size_t pos = 0;
+    std::string token;
+    while ((pos = s.find(delimiter)) != std::string::npos) {
+        token = s.substr(0, pos);
+        tokens.push_back(token);
+        s.erase(0, pos + delimiter.length());
+    }
+    tokens.push_back(s);
+    return tokens;
+}
+
 //
 // gguf utils
 //
@@ -271,3 +336,9 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
             return gguf_data_to_str(type, gguf_get_val_data(ctx_gguf, i), 0);
     }
 }
+
+//
+// API used internally with mtmd
+//
+
+projector_type clip_get_projector_type(const struct clip_ctx * ctx);

examples/llava/clip.cpp

@@ -32,23 +32,6 @@ struct clip_logger_state g_logger_state = {GGML_LOG_LEVEL_CONT, clip_log_callbac
 
 //#define CLIP_DEBUG_FUNCTIONS
 
-// RGB uint8 image
-struct clip_image_u8 {
-    int nx;
-    int ny;
-
-    std::vector<uint8_t> buf;
-};
-
-// RGB float32 image (NHWC)
-// Memory layout: RGBRGBRGB...
-struct clip_image_f32 {
-    int nx;
-    int ny;
-
-    std::vector<float> buf;
-};
-
 #ifdef CLIP_DEBUG_FUNCTIONS
 static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) {
     std::ofstream file(filename, std::ios::binary);
@@ -332,21 +315,21 @@ struct clip_ctx {
     bool use_gelu = false;
     bool use_silu = false;
 
-    struct gguf_context * ctx_gguf = nullptr;
-    struct ggml_context * ctx_data = nullptr;
+    gguf_context_ptr ctx_gguf;
+    ggml_context_ptr ctx_data;
 
     std::vector<uint8_t> buf_compute_meta;
 
     std::vector<ggml_backend_t> backend_ptrs;
     std::vector<ggml_backend_buffer_type_t> backend_buft;
 
-    ggml_backend_t backend     = nullptr;
-    ggml_backend_t backend_cpu = nullptr;
-    ggml_backend_buffer_t buf  = nullptr;
+    ggml_backend_t backend;
+    ggml_backend_t backend_cpu;
+    ggml_backend_buffer_ptr buf;
 
     ggml_backend_sched_ptr sched;
 
-    struct clip_image_size * load_image_size = nullptr;
+    clip_image_size load_image_size;
 
     clip_ctx(clip_context_params & ctx_params) {
         backend_cpu = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr);
@@ -372,18 +355,14 @@ struct clip_ctx {
     }
 
     ~clip_ctx() {
-        ggml_free(ctx_data);
-        gguf_free(ctx_gguf);
-        ggml_backend_buffer_free(buf);
         ggml_backend_free(backend);
-        if (backend_cpu != backend) {
+        if (backend != backend_cpu) {
             ggml_backend_free(backend_cpu);
         }
-        clip_image_size_free(load_image_size);
     }
 };
 
-static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_image_f32_batch * imgs) {
+static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_image_f32_batch & imgs) {
     const auto & model = ctx->vision_model;
     const auto & hparams = model.hparams;
 
@@ -399,7 +378,7 @@ static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_im
     const int n_layer              = hparams.n_layer;
     const float eps                = hparams.eps;
 
-    GGML_ASSERT(imgs->size == 1); // batch_size == 1
+    GGML_ASSERT(imgs.entries.size() == 1); // batch_size == 1
 
     struct ggml_init_params params = {
         /*.mem_size   =*/ ctx->buf_compute_meta.size(),
@@ -407,7 +386,9 @@ static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_im
         /*.no_alloc   =*/ true,
     };
 
-    struct ggml_context * ctx0 = ggml_init(params);
+    ggml_context_ptr ctx0_ptr(ggml_init(params));
+    auto ctx0 = ctx0_ptr.get();
+
     struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
     // input raw
@@ -529,12 +510,10 @@ static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_im
     // build the graph
     ggml_build_forward_expand(gf, embeddings);
 
-    ggml_free(ctx0);
-
     return gf;
 }
 
-static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_image_f32_batch * imgs, struct clip_image_size * load_image_size, bool is_inf = false) {
+static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_image_f32_batch & imgs, struct clip_image_size load_image_size, bool is_inf = false) {
     if (!ctx->has_vision_encoder) {
         LOG_ERR("This gguf file seems to have no vision encoder\n");
         return nullptr;
@@ -547,23 +526,20 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
     int image_size_width  = image_size;
     int image_size_height = image_size;
     if (ctx->has_minicpmv_projector) {
-        if (load_image_size == nullptr) {
-            load_image_size = clip_image_size_init();
-        }
-        LOG_DBG("%s: %d %d\n", __func__, load_image_size->width, load_image_size->height);
-        image_size_width  = load_image_size->width;
-        image_size_height = load_image_size->height;
+        LOG_DBG("%s: %d %d\n", __func__, load_image_size.width, load_image_size.height);
+        image_size_width  = load_image_size.width;
+        image_size_height = load_image_size.height;
         if (is_inf) {
-            image_size_width  = imgs->data->nx;
-            image_size_height = imgs->data->ny;
+            image_size_width  = imgs.entries[0]->nx;
+            image_size_height = imgs.entries[0]->ny;
         }
     }
     else if (ctx->has_qwen2vl_merger) {
         // use the image's native resolution when image is avaible
         if (is_inf) {
         // if (imgs->data->nx && imgs->data->ny) {
-            image_size_width  = imgs->data->nx;
-            image_size_height = imgs->data->ny;
+            image_size_width  = imgs.entries[0]->nx;
+            image_size_height = imgs.entries[0]->ny;
         }
     }
     const int patch_size           = hparams.patch_size;
@@ -578,7 +554,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
     const float eps                = hparams.eps;
     int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
 
-    const int batch_size = imgs->size;
+    const int batch_size = imgs.entries.size();
 
     if (ctx->has_llava_projector || ctx->has_minicpmv_projector || ctx->has_glm_projector) {
         GGML_ASSERT(batch_size == 1);
@@ -590,7 +566,9 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
         /*.no_alloc   =*/ true,
     };
 
-    struct ggml_context * ctx0 = ggml_init(params);
+    ggml_context_ptr ctx0_ptr(ggml_init(params));
+    auto ctx0 = ctx0_ptr.get();
+
     struct ggml_cgraph * gf = ggml_new_graph(ctx0);
 
     struct ggml_tensor * inp_raw = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, image_size_width, image_size_height, 3, batch_size);
@@ -1078,7 +1056,7 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
                 embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings);
             }
         } else {
-            GGML_ABORT("fatel error");
+            GGML_ABORT("fatal error");
         }
     }
     else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
@@ -1098,12 +1076,10 @@ static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_im
     // build the graph
     ggml_build_forward_expand(gf, embeddings);
 
-    ggml_free(ctx0);
-
     return gf;
 }
 
-static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs, struct clip_image_size * load_image_size, bool is_inf = false) {
+static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch & imgs, struct clip_image_size load_image_size, bool is_inf = false) {
     if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) {
         return clip_image_build_graph_siglip(ctx, imgs);
     } else {
@@ -1274,7 +1250,7 @@ struct clip_model_loader {
             /*.mem_buffer =*/ NULL,
             /*.no_alloc =*/ true,
         };
-        ctx_clip.ctx_data = ggml_init(params);
+        ctx_clip.ctx_data.reset(ggml_init(params));
         if (!ctx_clip.ctx_data) {
             throw std::runtime_error(string_format("%s: failed to init ggml context\n", __func__));
         }
@@ -1288,7 +1264,7 @@ struct clip_model_loader {
             if (cur) {
                 tensors_to_load.push_back(cur);
                 // add tensors to context
-                struct ggml_tensor * data_tensor = ggml_dup_tensor(ctx_clip.ctx_data, cur);
+                struct ggml_tensor * data_tensor = ggml_dup_tensor(ctx_clip.ctx_data.get(), cur);
                 ggml_set_name(data_tensor, cur->name);
                 cur = data_tensor;
             }
@@ -1460,10 +1436,10 @@ struct clip_model_loader {
 
             // alloc memory and offload data
             ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(ctx_clip.backend);
-            ctx_clip.buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx_clip.ctx_data, buft);
-            ggml_backend_buffer_set_usage(ctx_clip.buf, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
+            ctx_clip.buf.reset(ggml_backend_alloc_ctx_tensors_from_buft(ctx_clip.ctx_data.get(), buft));
+            ggml_backend_buffer_set_usage(ctx_clip.buf.get(), GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
             for (auto & t : tensors_to_load) {
-                struct ggml_tensor * cur = ggml_get_tensor(ctx_clip.ctx_data, t->name);
+                struct ggml_tensor * cur = ggml_get_tensor(ctx_clip.ctx_data.get(), t->name);
                 const size_t offset = tensor_offset[t->name];
                 fin.seekg(offset, std::ios::beg);
                 if (!fin) {
@@ -1488,10 +1464,20 @@ struct clip_model_loader {
 
     void alloc_compute_meta() {
         ctx_clip.buf_compute_meta.resize(GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead());
+
+        // create a fake batch
         clip_image_f32_batch batch;
-        batch.size = 1;
-        batch.data = nullptr;
-        ggml_cgraph * gf = clip_image_build_graph(&ctx_clip, &batch, nullptr, false);
+        clip_image_f32_ptr img(clip_image_f32_init());
+        clip_image_size image_size;
+        image_size.width  = clip_get_image_size(&ctx_clip);
+        image_size.height = clip_get_image_size(&ctx_clip);
+        int n_patches = clip_get_image_size(&ctx_clip) / image_size.width;
+        img->nx = n_patches;
+        img->ny = n_patches;
+        img->buf.resize(n_patches * image_size.width * image_size.height * 3);
+        batch.entries.push_back(std::move(img));
+
+        ggml_cgraph * gf = clip_image_build_graph(&ctx_clip, batch, image_size, false);
         ggml_backend_sched_reserve(ctx_clip.sched.get(), gf);
         for (size_t i = 0; i < ctx_clip.backend_ptrs.size(); ++i) {
             ggml_backend_t backend = ctx_clip.backend_ptrs[i];
@@ -1592,11 +1578,11 @@ struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_p
 }
 
 void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size) {
-    ctx_clip->load_image_size = load_image_size;
+    ctx_clip->load_image_size = *load_image_size; // copy
 }
 
 struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip) {
-    return ctx_clip->load_image_size;
+    return &ctx_clip->load_image_size;
 }
 
 struct clip_image_size * clip_image_size_init() {
@@ -1614,25 +1600,53 @@ struct clip_image_f32 * clip_image_f32_init() {
     return new clip_image_f32();
 }
 
+struct clip_image_f32_batch * clip_image_f32_batch_init() {
+    return new clip_image_f32_batch();
+}
+
+unsigned char * clip_image_u8_get_data(struct clip_image_u8 * img, uint32_t * nx, uint32_t * ny) {
+    if (nx) *nx = img->nx;
+    if (ny) *ny = img->ny;
+    return img->buf.data();
+}
+
 void clip_image_size_free(struct clip_image_size * load_image_size) {
     if (load_image_size == nullptr) {
         return;
     }
     delete load_image_size;
 }
-void clip_image_u8_free(struct clip_image_u8  * img) { delete img; }
-void clip_image_f32_free(struct clip_image_f32 * img) { delete img; }
-void clip_image_u8_batch_free(struct clip_image_u8_batch  * batch) {
-    if (batch->size > 0) {
-        delete[] batch->data;
-        batch->size = 0;
-    }
+void clip_image_u8_free(struct clip_image_u8  * img) { if (img) delete img; }
+void clip_image_f32_free(struct clip_image_f32 * img) { if (img) delete img; }
+void clip_image_u8_batch_free(struct clip_image_u8_batch * batch) { if (batch) delete batch; }
+void clip_image_f32_batch_free(struct clip_image_f32_batch * batch) { if (batch) delete batch; }
+
+size_t clip_image_f32_batch_n_images(const struct clip_image_f32_batch * batch) {
+    return batch->entries.size();
 }
-void clip_image_f32_batch_free(struct clip_image_f32_batch  * batch) {
-    if (batch->size > 0) {
-        delete[] batch->data;
-        batch->size = 0;
+
+size_t clip_image_f32_batch_nx(const struct clip_image_f32_batch * batch, int idx) {
+    if (idx < 0 || idx >= (int)batch->entries.size()) {
+        LOG_ERR("%s: invalid index %d\n", __func__, idx);
+        return 0;
     }
+    return batch->entries[idx]->nx;
+}
+
+size_t clip_image_f32_batch_ny(const struct clip_image_f32_batch * batch, int idx) {
+    if (idx < 0 || idx >= (int)batch->entries.size()) {
+        LOG_ERR("%s: invalid index %d\n", __func__, idx);
+        return 0;
+    }
+    return batch->entries[idx]->ny;
+}
+
+clip_image_f32 * clip_image_f32_get_img(const struct clip_image_f32_batch * batch, int idx) {
+    if (idx < 0 || idx >= (int)batch->entries.size()) {
+        LOG_ERR("%s: invalid index %d\n", __func__, idx);
+        return nullptr;
+    }
+    return batch->entries[idx].get();
 }
 
 void clip_build_img_from_pixels(const unsigned char * rgb_pixels, int nx, int ny, clip_image_u8 * img) {
@@ -1706,14 +1720,15 @@ static void bilinear_resize(const clip_image_u8& src, clip_image_u8& dst, int ta
 }
 
 // Normalize image to float32 - careful with pytorch .to(model.device, dtype=torch.float16) - this sometimes reduces precision (32>16>32), sometimes not
-static void normalize_image_u8_to_f32(const clip_image_u8* src, clip_image_f32* dst, const float mean[3], const float std[3]) {
-    dst->nx = src->nx;
-    dst->ny = src->ny;
-    dst->buf.resize(src->buf.size());
+static void normalize_image_u8_to_f32(const clip_image_u8 & src, clip_image_f32 & dst, const float mean[3], const float std[3]) {
+    dst.nx = src.nx;
+    dst.ny = src.ny;
+    dst.buf.resize(src.buf.size());
 
-    for (size_t i = 0; i < src->buf.size(); ++i) {
+    // TODO @ngxson : seems like this could be done more efficiently on cgraph
+    for (size_t i = 0; i < src.buf.size(); ++i) {
         int c = i % 3; // rgb
-        dst->buf[i] = (static_cast<float>(src->buf[i]) / 255.0f - mean[c]) / std[c];
+        dst.buf[i] = (static_cast<float>(src.buf[i]) / 255.0f - mean[c]) / std[c];
     }
 }
 
@@ -1721,7 +1736,7 @@ inline int clip(int x, int lower, int upper) {
     return std::max(lower, std::min(x, upper));
 }
 
-static bool bicubic_resize(const clip_image_u8 &img, clip_image_u8 &dst, int target_width, int target_height) {
+static bool bicubic_resize(const clip_image_u8 & img, clip_image_u8 & dst, int target_width, int target_height) {
     const int nx = img.nx;
     const int ny = img.ny;
 
@@ -1859,13 +1874,13 @@ static std::pair<int, int> select_best_resolution(const std::pair<int, int> & or
     return best_fit;
 }
 
-static std::vector<clip_image_u8*> divide_to_patches_u8(const clip_image_u8 & image, int patch_size) {
-    std::vector<clip_image_u8*> patches;
+static std::vector<clip_image_u8_ptr> divide_to_patches_u8(const clip_image_u8 & image, int patch_size) {
+    std::vector<clip_image_u8_ptr> patches;
     int width = image.nx;
     int height = image.ny;
     for (int i = 0; i < height; i += patch_size) {
         for (int j = 0; j < width; j += patch_size) {
-            clip_image_u8 *patch = clip_image_u8_init();
+            clip_image_u8_ptr patch(clip_image_u8_init());
             patch->nx = std::min(patch_size, width - j);
             patch->ny = std::min(patch_size, height - i);
             patch->buf.resize(3 * patch->nx * patch->ny);
@@ -1876,7 +1891,7 @@ static std::vector<clip_image_u8*> divide_to_patches_u8(const clip_image_u8 & im
                     }
                 }
             }
-            patches.push_back(patch);
+            patches.push_back(std::move(patch));
         }
     }
     return patches;
@@ -1957,7 +1972,7 @@ static std::pair<int, int> uhd_best_grid(const int max_slice_nums, const int mul
 //    -> https://arxiv.org/pdf/2403.11703
 //    -> https://github.com/thunlp/LLaVA-UHD
 //    -> https://github.com/thunlp/LLaVA-UHD/blob/302301bc2175f7e717fb8548516188e89f649753/llava_uhd/train/llava-uhd/slice_logic.py#L118
-static std::vector<std::vector<clip_image_u8 *>> uhd_slice_image(const clip_image_u8 * img, const int max_slice_nums=9, const int scale_resolution=448, const int patch_size=14) {
+static std::vector<std::vector<clip_image_u8_ptr>> uhd_slice_image(const clip_image_u8 * img, const int max_slice_nums=9, const int scale_resolution=448, const int patch_size=14) {
     const std::pair<int, int> original_size={img->nx,img->ny};
     const int original_width = img->nx;
     const int original_height = img->ny;
@@ -1965,30 +1980,30 @@ static std::vector<std::vector<clip_image_u8 *>> uhd_slice_image(const clip_imag
     const float ratio = 1.0 * original_width * original_height/ (scale_resolution * scale_resolution);
     const int multiple = fmin(ceil(ratio), max_slice_nums);
 
-    std::vector<std::vector<clip_image_u8 *>> images;
+    std::vector<std::vector<clip_image_u8_ptr>> images;
     LOG_DBG("%s: multiple %d\n", __func__, multiple);
-    images.push_back(std::vector<clip_image_u8 *>());
+    images.push_back(std::vector<clip_image_u8_ptr>());
 
     if (multiple <= 1) {
         auto best_size = uhd_find_best_resize(original_size, scale_resolution, patch_size, true);
-        clip_image_u8 * source_image = clip_image_u8_init();
+        clip_image_u8_ptr source_image(clip_image_u8_init());
         bicubic_resize(*img, *source_image, best_size.first, best_size.second);
         // source_image = image.resize(best_size, Image.Resampling.BICUBIC)
-        images[images.size()-1].push_back(source_image);
+        images.back().push_back(std::move(source_image));
     }
     else if (multiple > 1) {
         auto best_size = uhd_find_best_resize(original_size, scale_resolution, patch_size);
-        clip_image_u8 * source_image = clip_image_u8_init();
+        clip_image_u8_ptr source_image(clip_image_u8_init());
         bicubic_resize(*img, *source_image, best_size.first, best_size.second);
         // source_image = image.copy().resize(best_resize, Image.Resampling.BICUBIC)
         LOG_DBG("%s: image_size: %d %d; source_image size: %d %d\n", __func__, img->nx, img->ny, best_size.first, best_size.second);
-        images[images.size()-1].push_back(source_image);
+        images.back().push_back(std::move(source_image));
 
         std::pair<int, int> best_grid = uhd_best_grid(max_slice_nums, multiple, log_ratio);
         LOG_DBG("%s: image_size: %d %d; best_grid: %d %d\n", __func__, img->nx, img->ny, best_grid.first, best_grid.second);
 
         auto refine_size = uhd_get_refine_size(original_size, best_grid, scale_resolution, patch_size, true);
-        clip_image_u8 * refine_image = clip_image_u8_init();
+        clip_image_u8_ptr refine_image(clip_image_u8_init());
         bicubic_resize(*img, *refine_image, refine_size.first, refine_size.second);
 
         LOG_DBG("%s: refine_image_size: %d %d; refine_size: %d %d\n", __func__, refine_image->nx, refine_image->ny, refine_size.first, refine_size.second);
@@ -1999,9 +2014,9 @@ static std::vector<std::vector<clip_image_u8 *>> uhd_slice_image(const clip_imag
         int grid_x = int(width / best_grid.first);
         int grid_y = int(height / best_grid.second);
         for (int patches_i = 0, ic = 0; patches_i < height && ic < best_grid.second; patches_i += grid_y, ic += 1){
-            images.push_back(std::vector<clip_image_u8 *>());
+            images.push_back(std::vector<clip_image_u8_ptr>());
             for(int patches_j = 0, jc = 0; patches_j < width && jc < best_grid.first; patches_j += grid_x, jc += 1){
-                clip_image_u8 * patch = clip_image_u8_init();
+                clip_image_u8_ptr patch(clip_image_u8_init());
                 patch->nx = grid_x;
                 patch->ny = grid_y;
                 patch->buf.resize(3 * patch->nx * patch->ny);
@@ -2014,10 +2029,9 @@ static std::vector<std::vector<clip_image_u8 *>> uhd_slice_image(const clip_imag
                         patch->buf[j+2] = refine_image->buf[i+2];
                     }
                 }
-                images[images.size()-1].push_back(patch);
+                images.back().push_back(std::move(patch));
             }
         }
-        clip_image_u8_free(refine_image);
     }
     return images;
 }
@@ -2025,8 +2039,8 @@ static std::vector<std::vector<clip_image_u8 *>> uhd_slice_image(const clip_imag
 int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip) {
     const int max_slice_nums=9;
     const int scale_resolution=448;
-    const int original_width = ctx_clip->load_image_size->width;
-    const int original_height = ctx_clip->load_image_size->height;
+    const int original_width = ctx_clip->load_image_size.width;
+    const int original_height = ctx_clip->load_image_size.height;
     const float log_ratio = log(1.0*original_width/original_height);
     const float ratio = 1.0 * original_width * original_height/ (scale_resolution * scale_resolution);
     const int multiple = fmin(ceil(ratio), max_slice_nums);
@@ -2036,64 +2050,44 @@ int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip) {
 
 // returns the normalized float tensor for llava-1.5, for spatial_unpad with anyres processing for llava-1.6 it returns the normalized image patch tensors as a vector
 // res_imgs memory is being allocated here, previous allocations will be freed if found
-bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) {
+bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, struct clip_image_f32_batch * res_imgs) {
 
-    if(clip_is_minicpmv(ctx)){
+    if (clip_is_minicpmv(ctx)) {
         int max_slice_nums = 9;
-        std::vector<std::vector<clip_image_u8 *>> imgs = uhd_slice_image(img, max_slice_nums);
-        res_imgs->size = 0;
-        for (size_t i = 0; i < imgs.size(); ++i){
-            res_imgs->size += imgs[i].size();
-        }
-        res_imgs->data = new clip_image_f32[res_imgs->size];
-        int idx = 0;
+        std::vector<std::vector<clip_image_u8_ptr>> imgs = uhd_slice_image(img, max_slice_nums);
         for (size_t i = 0; i < imgs.size(); ++i) {
             for (size_t j = 0; j < imgs[i].size(); ++j) {
                 LOG_DBG("%s: %d %d\n", __func__,imgs[i][j]->nx,imgs[i][j]->ny);
-                clip_image_f32 * res = clip_image_f32_init();
-                normalize_image_u8_to_f32(imgs[i][j], res, ctx->image_mean, ctx->image_std);
-                res_imgs->data[idx++] = *res;
-                clip_image_f32_free(res);
-            }
-        }
-        for (size_t i = 0; i < imgs.size(); ++i) {
-            for (size_t j = 0; j < imgs[i].size(); ++j) {
-                if (imgs[i][j] != nullptr) {
-                    clip_image_u8_free(imgs[i][j]);
-                }
+                clip_image_f32_ptr res(clip_image_f32_init());
+                normalize_image_u8_to_f32(*imgs[i][j], *res, ctx->image_mean, ctx->image_std);
+                res_imgs->entries.push_back(std::move(res));
             }
         }
         return true;
     }
     else if (ctx->has_qwen2vl_merger) {
-        clip_image_u8 * resized = clip_image_u8_init();
-        auto patch_size = clip_patch_size(ctx) * 2;
+        clip_image_u8 resized;
+        auto patch_size = clip_get_patch_size(ctx) * 2;
         int nx = ceil((float)img->nx / patch_size) * patch_size;
         int ny = ceil((float)img->ny / patch_size) * patch_size;
-        bicubic_resize(*img, *resized, nx, ny);
+        bicubic_resize(*img, resized, nx, ny);
 
-        res_imgs->data = new clip_image_f32[1];
-        // clip_image_f32 * res = clip_image_f32_init();
-        normalize_image_u8_to_f32(resized, res_imgs->data, ctx->image_mean, ctx->image_std);
+        clip_image_f32_ptr img_f32(clip_image_f32_init());
+        // clip_image_f32_ptr res(clip_image_f32_init());
+        normalize_image_u8_to_f32(resized, *img_f32, ctx->image_mean, ctx->image_std);
         // res_imgs->data[0] = *res;
-        res_imgs->size = 1;
-
-        // clip_image_f32_free(res);
-        clip_image_u8_free(resized);
+        res_imgs->entries.push_back(std::move(img_f32));
         return true;
     }
 
     if (ctx->has_glm_projector || ctx->proj_type == PROJECTOR_TYPE_GEMMA3) {
-        res_imgs->size = 1;
-        res_imgs->data = new clip_image_f32[res_imgs->size];
         clip_image_u8 resized_image;
         int32_t sz=ctx->vision_model.hparams.image_size;
         bicubic_resize(*img, resized_image,sz,sz);
-        clip_image_f32 * res = clip_image_f32_init();
+        clip_image_f32_ptr img_f32(clip_image_f32_init());
         //clip_image_save_to_bmp(resized_image, "resized.bmp");
-        normalize_image_u8_to_f32(&resized_image, res, ctx->image_mean, ctx->image_std);
-        res_imgs->data[0] = *res;
-        clip_image_f32_free(res);
+        normalize_image_u8_to_f32(resized_image, *img_f32, ctx->image_mean, ctx->image_std);
+        res_imgs->entries.push_back(std::move(img_f32));
         return true;
     }
 
@@ -2108,16 +2102,12 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
         pad_to_square = false;
     }
     // free the previous res_imgs if any set
-    if (res_imgs->size > 0) {
-        clip_image_f32_batch_free(res_imgs);
-    }
-    res_imgs->data = nullptr;
-    res_imgs->size = 0;
+    res_imgs->entries.clear();
 
     // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104)
     // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
 
-    clip_image_u8 * temp = clip_image_u8_init(); // we will keep the input image data here temporarily
+    clip_image_u8_ptr temp(clip_image_u8_init()); // we will keep the input image data here temporarily
     if (pad_to_square && img->nx != img->ny) {
         int longer_side = std::max(img->nx, img->ny);
         temp->nx = longer_side;
@@ -2160,28 +2150,18 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
             //     clip_image_u8_free(temp2);
             // }
 
-            std::vector<clip_image_u8 *> patches = divide_to_patches_u8(*temp, params.image_size); // prepare spatial sorted main patches of image_size each (336 in llava-1.6)
+            std::vector<clip_image_u8_ptr> patches = divide_to_patches_u8(*temp, params.image_size); // prepare spatial sorted main patches of image_size each (336 in llava-1.6)
 
-            clip_image_u8 *image_original_resize = clip_image_u8_init();
+            clip_image_u8_ptr image_original_resize(clip_image_u8_init());
             // bilinear_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square
             bicubic_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square
-            patches.insert(patches.begin(), image_original_resize);
-            // clip_image_f32_batch_init(patches.size());
-            res_imgs->size = patches.size();
-            res_imgs->data = new clip_image_f32[res_imgs->size];
-            int num=0;
-            for (auto& patch : patches) {
-                normalize_image_u8_to_f32(patch, &res_imgs->data[num], ctx->image_mean, ctx->image_std);
-                num++;
+            patches.insert(patches.begin(), std::move(image_original_resize));
+            for (auto & patch : patches) {
+                clip_image_f32_ptr res(clip_image_f32_init());
+                normalize_image_u8_to_f32(*patch, *res, ctx->image_mean, ctx->image_std);
+                res_imgs->entries.push_back(std::move(res));
             }
 
-            for (size_t i = 0; i < patches.size(); i++) {
-                // LOG_DBG("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
-                clip_image_u8_free(patches[i]);
-            }
-
-            clip_image_u8_free(temp);
-
             return true;
         } else {
             temp->nx = img->nx;
@@ -2197,7 +2177,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
 
     const int nx2 = ctx->vision_model.hparams.image_size;
     const int ny2 = ctx->vision_model.hparams.image_size;
-    clip_image_f32 * res = clip_image_f32_init();
+    clip_image_f32_ptr res(clip_image_f32_init());
     res->nx = nx2;
     res->ny = ny2;
     res->buf.resize(3 * nx2 * ny2);
@@ -2249,7 +2229,6 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
             }
         }
     }
-    clip_image_u8_free(temp);
 
     // {
     //     clip_image_u8 * temp2 = clip_image_u8_init();
@@ -2259,10 +2238,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
     // }
     // res_imgs.push_back(res);
 
-    res_imgs->size = 1;
-    res_imgs->data = new clip_image_f32[res_imgs->size];
-    res_imgs->data[0] = *res;
-    clip_image_f32_free(res);
+    res_imgs->entries.push_back(std::move(res));
 
     return true;
 }
@@ -2290,15 +2266,15 @@ size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w
     return clip_n_patches_by_img(ctx, &img) * clip_n_mmproj_embd(ctx) * sizeof(float);
 }
 
-int32_t clip_image_size(const struct clip_ctx * ctx) {
+int32_t clip_get_image_size(const struct clip_ctx * ctx) {
     return ctx->vision_model.hparams.image_size;
 }
 
-int32_t clip_patch_size(const struct clip_ctx * ctx) {
+int32_t clip_get_patch_size(const struct clip_ctx * ctx) {
     return ctx->vision_model.hparams.patch_size;
 }
 
-int32_t clip_hidden_size(const struct clip_ctx * ctx) {
+int32_t clip_get_hidden_size(const struct clip_ctx * ctx) {
     return ctx->vision_model.hparams.hidden_size;
 }
 
@@ -2346,6 +2322,8 @@ int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * i
         int x_patch = img->nx / patch_size + (int)(img->nx % patch_size > 0);
         int y_patch = img->ny / patch_size + (int)(img->ny % patch_size > 0);
         n_patches = x_patch * y_patch;
+    } else if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) {
+        n_patches = 256;
     }
 
     return n_patches;
@@ -2443,19 +2421,23 @@ bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f3
         return false;
     }
 
-    clip_image_f32_batch imgs{};
-    imgs.size = 1;
-    imgs.data = img;
+    clip_image_f32_batch imgs;
+    clip_image_f32_ptr img_copy(clip_image_f32_init());
+    *img_copy = *img;
+    imgs.entries.push_back(std::move(img_copy));
+
     return clip_image_batch_encode(ctx, n_threads, &imgs, vec);
 }
 
-bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) {
+bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs_c_ptr, float * vec) {
+    const clip_image_f32_batch & imgs = *imgs_c_ptr;
+
     if (!ctx->has_vision_encoder) {
         LOG_ERR("%s: This gguf file seems to have no vision encoder\n", __func__);
         return false;
     }
 
-    int batch_size = imgs->size;
+    int batch_size = imgs.entries.size();
     if (ctx->has_llava_projector) {
         GGML_ASSERT(batch_size == 1); // TODO: support multiple images
     }
@@ -2482,25 +2464,22 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     int image_size_width  = image_size;
     int image_size_height = image_size;
     if (ctx->has_minicpmv_projector | ctx->has_qwen2vl_merger) {
-        image_size_width  = imgs->data[0].nx;
-        image_size_height = imgs->data[0].ny;
+        image_size_width  = imgs.entries[0]->nx;
+        image_size_height = imgs.entries[0]->ny;
     }
     const int patch_size    = hparams.patch_size;
     const int num_patches   = ((image_size_width / patch_size) * (image_size_height / patch_size));
     const int num_positions = num_patches + (model.class_embedding ? 1 : 0);
-    if(ctx->load_image_size==nullptr){
-        ctx->load_image_size= clip_image_size_init();
-    }
-    const int pos_w = ctx->load_image_size->width/patch_size;
-    const int pos_h = ctx->load_image_size->height/patch_size;
+    const int pos_w = ctx->load_image_size.width / patch_size;
+    const int pos_h = ctx->load_image_size.height / patch_size;
 
     {
         struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw");
         float * data = (float *)malloc(ggml_nbytes(inp_raw));
 
-        for (size_t i = 0; i < imgs->size; i++) {
-            const int nx = imgs->data[i].nx;
-            const int ny = imgs->data[i].ny;
+        for (size_t i = 0; i < imgs.entries.size(); i++) {
+            const int nx = imgs.entries[i]->nx;
+            const int ny = imgs.entries[i]->ny;
             if (!(ctx->has_minicpmv_projector | ctx->has_qwen2vl_merger)) {
                 GGML_ASSERT(nx == image_size && ny == image_size);
             }
@@ -2511,7 +2490,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
                 for (int k = 0; k < 3; k++) {
                     for (int y = 0; y < ny; y++) {
                         for (int x = 0; x < nx; x++) {
-                            data[(b * 3 * n) + k * n + y * nx + x] = imgs->data[b].buf[3 * (y * nx + x) + k];
+                            data[(b * 3 * n) + k * n + y * nx + x] = imgs.entries[b]->buf[3 * (y * nx + x) + k];
                         }
                     }
                 }
@@ -2671,8 +2650,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
         /* verbosity */ GGML_LOG_LEVEL_ERROR,
     });
 
-    const auto & ctx_src = ctx_clip->ctx_gguf;
-    const auto & ctx_data = ctx_clip->ctx_data;
+    const auto & ctx_src = ctx_clip->ctx_gguf.get();
+    const auto & ctx_data = ctx_clip->ctx_data.get();
 
     auto * ctx_out = gguf_init_empty();
     gguf_set_kv(ctx_out, ctx_src);
@@ -2893,3 +2872,11 @@ bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img,
     clip_image_encode(ctx, n_threads, &clip_img, vec);
     return true;
 }
+
+//
+// API used internally with mtmd
+//
+
+projector_type clip_get_projector_type(const struct clip_ctx * ctx) {
+    return ctx->proj_type;
+}

examples/llava/clip.h

@@ -30,15 +30,8 @@ struct clip_image_size {
     int height;
 };
 
-struct clip_image_u8_batch {
-    struct clip_image_u8 * data;
-    size_t size;
-};
-
-struct clip_image_f32_batch {
-    struct clip_image_f32 * data;
-    size_t size;
-};
+struct clip_image_u8_batch;
+struct clip_image_f32_batch;
 
 struct clip_context_params {
     bool use_gpu;
@@ -55,9 +48,9 @@ CLIP_API void clip_free(struct clip_ctx * ctx);
 CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx);
 CLIP_API size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w);
 
-CLIP_API int32_t clip_image_size (const struct clip_ctx * ctx);
-CLIP_API int32_t clip_patch_size (const struct clip_ctx * ctx);
-CLIP_API int32_t clip_hidden_size(const struct clip_ctx * ctx);
+CLIP_API int32_t clip_get_image_size (const struct clip_ctx * ctx);
+CLIP_API int32_t clip_get_patch_size (const struct clip_ctx * ctx);
+CLIP_API int32_t clip_get_hidden_size(const struct clip_ctx * ctx);
 
 // TODO: should be enum, not string
 CLIP_API const char * clip_patch_merge_type(const struct clip_ctx * ctx);
@@ -73,9 +66,13 @@ CLIP_API int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip);
 CLIP_API void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size);
 CLIP_API struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip);
 
-CLIP_API struct clip_image_size * clip_image_size_init();
-CLIP_API struct clip_image_u8  * clip_image_u8_init ();
-CLIP_API struct clip_image_f32 * clip_image_f32_init();
+CLIP_API struct clip_image_size      * clip_image_size_init();
+CLIP_API struct clip_image_u8        * clip_image_u8_init ();
+CLIP_API struct clip_image_f32       * clip_image_f32_init();
+CLIP_API struct clip_image_f32_batch * clip_image_f32_batch_init(); // only used by libllava
+
+// nx, ny are the output image dimensions
+CLIP_API unsigned char * clip_image_u8_get_data(struct clip_image_u8 * img, uint32_t * nx, uint32_t * ny);
 
 CLIP_API void clip_image_size_free (struct clip_image_size * img_size);
 CLIP_API void clip_image_u8_free (struct clip_image_u8  * img);
@@ -83,6 +80,12 @@ CLIP_API void clip_image_f32_free(struct clip_image_f32 * img);
 CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch  * batch);
 CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch * batch);
 
+// use for accessing underlay data of clip_image_f32_batch
+CLIP_API size_t clip_image_f32_batch_n_images(const struct clip_image_f32_batch * batch); // equivalent to batch->size()
+CLIP_API size_t clip_image_f32_batch_nx(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->nx
+CLIP_API size_t clip_image_f32_batch_ny(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->ny
+CLIP_API clip_image_f32 * clip_image_f32_get_img(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->data
+
 /**
  * Build image from pixels decoded by other libraries instead of stb_image.h for better performance.
  * The memory layout is RGBRGBRGB..., input buffer length must be 3*nx*ny bytes

examples/llava/gemma3-cli.cpp

@@ -2,11 +2,11 @@
 #include "log.h"
 #include "common.h"
 #include "sampling.h"
-#include "clip.h"
-#include "stb_image.h"
 #include "llama.h"
 #include "ggml.h"
 #include "console.h"
+#include "chat.h"
+#include "mtmd.h"
 
 #include <vector>
 #include <limits.h>
@@ -57,13 +57,18 @@ static void sigint_handler(int signo) {
 #endif
 
 struct gemma3_context {
-    struct clip_ctx    * ctx_clip = NULL;
-    common_init_result   llama_init;
+    mtmd_context_ptr ctx_vision;
+    common_init_result llama_init;
 
     llama_model       * model;
     llama_context     * lctx;
     const llama_vocab * vocab;
     llama_batch         batch;
+    int                 n_batch;
+
+    // note: we know that gemma3 template is "linear", meaning each turn is completely separated to another
+    // so here we don't need to keep track of chat history
+    common_chat_templates_ptr tmpls;
 
     int n_threads    = 1;
     llama_pos n_past = 0;
@@ -74,21 +79,24 @@ struct gemma3_context {
         vocab = llama_model_get_vocab(model);
         n_threads = params.cpuparams.n_threads;
         batch = llama_batch_init(params.n_batch, 0, 1);
-        init_clip_model(params);
+        n_batch = params.n_batch;
+        tmpls = common_chat_templates_init(model, params.chat_template);
+        init_vision_context(params);
     }
 
-    void init_clip_model(common_params & params) {
+    void init_vision_context(common_params & params) {
         const char * clip_path = params.mmproj.path.c_str();
-        ctx_clip = clip_model_load(clip_path, GGML_LOG_LEVEL_INFO);
-        if (!ctx_clip) {
-            LOG_ERR("Failed to load CLIP model from %s\n", clip_path);
+        ctx_vision.reset(mtmd_init_from_file(clip_path, model, mtmd_context_params{
+            /* use_gpu */   true,
+            /* timings */   true,
+            /* n_threads */ params.cpuparams.n_threads,
+            /* verbosity */ GGML_LOG_LEVEL_INFO,
+        }));
+        if (!ctx_vision.get()) {
+            LOG_ERR("Failed to load vision model from %s\n", clip_path);
             exit(1);
         }
     }
-
-    ~gemma3_context() {
-        clip_free(ctx_clip);
-    }
 };
 
 struct decode_embd_batch {
@@ -124,77 +132,6 @@ struct decode_embd_batch {
     }
 };
 
-static int eval_text(gemma3_context & ctx, std::string input, bool logits_last = false) {
-    llama_tokens tokens = common_tokenize(ctx.lctx, input, false, true);
-    common_batch_clear(ctx.batch);
-    for (llama_token & t : tokens) {
-        common_batch_add(ctx.batch, t, ctx.n_past++, {0}, false);
-    }
-    if (logits_last) {
-        ctx.batch.logits[ctx.batch.n_tokens - 1] = true;
-    }
-    // LOG("eval_text (n_tokens = %d): %s\n", (int)tokens.size(), input.c_str());
-    if (llama_decode(ctx.lctx, ctx.batch)) {
-        LOG_ERR("Failed to decode text\n");
-        return 1;
-    }
-    return 0;
-}
-
-static int eval_image(gemma3_context & ctx, std::string & fname) {
-    std::vector<float> image_embd_v;
-    int n_embd = llama_model_n_embd(ctx.model);
-    int n_tokens = 256;
-    image_embd_v.resize(n_tokens * n_embd);
-
-    bool ok;
-    struct clip_image_u8 * img_u8 = clip_image_u8_init();
-    ok = clip_image_load_from_file(fname.c_str(), img_u8);
-    if (!ok) {
-        LOG_ERR("Unable to load image %s\n", fname.c_str());
-        clip_image_u8_free(img_u8);
-        return 2; // non-fatal error
-    }
-
-    clip_image_f32_batch batch_f32;
-    ok = clip_image_preprocess(ctx.ctx_clip, img_u8, &batch_f32);
-    if (!ok) {
-        LOG_ERR("Unable to preprocess image\n");
-        clip_image_f32_batch_free(&batch_f32);
-        clip_image_u8_free(img_u8);
-        return 1;
-    }
-
-    int64_t t0 = ggml_time_ms();
-    LOG("Encoding image %s\n", fname.c_str());
-    ok = clip_image_batch_encode(ctx.ctx_clip, ctx.n_threads, &batch_f32, image_embd_v.data());
-    if (!ok) {
-        LOG_ERR("Unable to encode image\n");
-        clip_image_f32_batch_free(&batch_f32);
-        clip_image_u8_free(img_u8);
-        return 1;
-    }
-    LOG("Image encoded in %" PRId64 " ms\n", ggml_time_ms() - t0);
-
-    clip_image_f32_batch_free(&batch_f32);
-    clip_image_u8_free(img_u8);
-
-    // decode image embeddings
-    int64_t t1 = ggml_time_ms();
-    eval_text(ctx, "<start_of_image>");
-    llama_set_causal_attn(ctx.lctx, false);
-    decode_embd_batch batch_img(image_embd_v.data(), n_tokens, ctx.n_past, 0);
-    if (llama_decode(ctx.lctx, batch_img.batch)) {
-        LOG_ERR("failed to decode image\n");
-        return 1;
-    }
-    ctx.n_past += n_tokens;
-    llama_set_causal_attn(ctx.lctx, true);
-    eval_text(ctx, "<end_of_image>");
-    LOG("Image decoded in %" PRId64 " ms\n", ggml_time_ms() - t1);
-    return 0;
-}
-
 static int generate_response(gemma3_context & ctx, common_sampler * smpl, int n_predict) {
     for (int i = 0; i < n_predict; i++) {
         if (i > n_predict || !g_is_generating) {
@@ -224,6 +161,45 @@ static int generate_response(gemma3_context & ctx, common_sampler * smpl, int n_
     return 0;
 }
 
+static int eval_message(gemma3_context & ctx, common_chat_msg & msg, std::vector<std::string> & images_fname, bool add_bos = false) {
+    std::vector<mtmd_bitmap> bitmaps;
+
+    common_chat_templates_inputs tmpl_inputs;
+    tmpl_inputs.messages = {msg};
+    tmpl_inputs.add_generation_prompt = true;
+    tmpl_inputs.use_jinja = false; // jinja is buggy here
+    auto formatted_chat = common_chat_templates_apply(ctx.tmpls.get(), tmpl_inputs);
+    LOG_DBG("formatted_chat.prompt: %s\n", formatted_chat.prompt.c_str());
+
+    for (auto & fname : images_fname) {
+        mtmd_bitmap bitmap;
+        if (mtmd_helper_bitmap_init_from_file(fname.c_str(), bitmap)) {
+            LOG_ERR("Unable to load image %s\n", fname.c_str());
+            return 2; // image not found
+        }
+        bitmaps.push_back(std::move(bitmap));
+    }
+
+    mtmd_input_text text;
+    text.text          = formatted_chat.prompt;
+    text.add_special   = add_bos;
+    text.parse_special = true;
+    mtmd_input_chunks_ptr chunks(mtmd_tokenize(ctx.ctx_vision.get(), text, bitmaps));
+    if (chunks == nullptr) {
+        LOG_ERR("Unable to tokenize prompt\n");
+        return 1;
+    }
+
+    if (mtmd_helper_eval(ctx.ctx_vision.get(), ctx.lctx, chunks.get(), ctx.n_past, 0, ctx.n_batch)) {
+        LOG_ERR("Unable to eval prompt\n");
+        return 1;
+    }
+
+    ctx.n_past += mtmd_helper_get_n_tokens(chunks.get());
+
+    return 0;
+}
+
 int main(int argc, char ** argv) {
     ggml_time_init();
 
@@ -265,21 +241,15 @@ int main(int argc, char ** argv) {
 #endif
     }
 
-    if (eval_text(ctx, "<bos>")) {
-        return 1;
-    }
-
     if (is_single_turn) {
         g_is_generating = true;
-        if (eval_text(ctx, "<start_of_turn>user\n")) {
-            return 1;
+        if (params.prompt.find("<__image__>") == std::string::npos) {
+            params.prompt += " <__image__>";
         }
-        for (auto & fname : params.image) {
-            if (eval_image(ctx, fname)) {
-                return 1;
-            }
-        }
-        if (eval_text(ctx, params.prompt + "<end_of_turn><start_of_turn>model\n", true)) {
+        common_chat_msg msg;
+        msg.role = "user";
+        msg.content = params.prompt;
+        if (eval_message(ctx, msg, params.image, true)) {
             return 1;
         }
         if (generate_response(ctx, smpl, n_predict)) {
@@ -293,9 +263,9 @@ int main(int argc, char ** argv) {
         LOG("\n   /quit or /exit   exit the program");
         LOG("\n");
 
-        if (eval_text(ctx, "<start_of_turn>user\n")) {
-            return 1;
-        }
+        bool is_first_msg = true;
+        std::vector<std::string> images_fname;
+        std::string content;
 
         while (true) {
             g_is_generating = false;
@@ -320,26 +290,33 @@ int main(int argc, char ** argv) {
             g_is_generating = true;
             if (line.find("/image") == 0) {
                 std::string image = line.substr(7);
-                int res = eval_image(ctx, image);
-                if (res == 2) {
-                    continue; // image not found
-                }
-                if (res) {
-                    return 1;
-                }
+                images_fname.push_back(string_strip(image));
+                content += "<__image__>";
+                continue;
+            } else {
+                content += line;
+            }
+            common_chat_msg msg;
+            msg.role = "user";
+            msg.content = content;
+            int ret = eval_message(ctx, msg, images_fname, is_first_msg);
+            if (ret == 2) {
+                // non-fatal error
+                images_fname.clear();
+                content.clear();
                 continue;
             }
-            if (eval_text(ctx, line + "<end_of_turn><start_of_turn>model\n", true)) {
+            if (ret) {
                 return 1;
             }
             if (generate_response(ctx, smpl, n_predict)) {
                 return 1;
             }
-            if (eval_text(ctx, "<end_of_turn><start_of_turn>user\n")) {
-                return 1;
-            }
+            images_fname.clear();
+            content.clear();
+            is_first_msg = false;
         }
     }
-
+    llama_perf_context_print(ctx.lctx);
     return 0;
 }

examples/llava/llava.cpp

@@ -10,6 +10,7 @@
 #include <cstring>
 #include <limits>
 #include <vector>
+#include <memory>
 
 #if defined(LLAVA_LOG_OFF)
 #   define LOG_INF(...)
@@ -45,6 +46,17 @@ struct clip_image_grid_shape {
     int second;
 };
 
+// convenience cpp wrapper
+struct clip_image_f32_batch_deleter {
+    void operator()(clip_image_f32_batch * val) { clip_image_f32_batch_free(val); }
+};
+typedef std::unique_ptr<clip_image_f32_batch, clip_image_f32_batch_deleter> clip_image_f32_batch_ptr;
+
+struct clip_image_size_deleter {
+    void operator()(clip_image_f32_batch * val) { clip_image_f32_batch_free(val); }
+};
+typedef std::unique_ptr<clip_image_size, clip_image_size_deleter> clip_image_size_ptr;
+
 /**
  * Selects the best resolution from a list of possible resolutions based on the original size.
  *
@@ -105,8 +117,8 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
         struct ggml_context * ctx;
     } model;
 
-    const int32_t image_size = clip_image_size(ctx_clip);
-    const int32_t patch_size = clip_patch_size(ctx_clip);
+    const int32_t image_size = clip_get_image_size(ctx_clip);
+    const int32_t patch_size = clip_get_patch_size(ctx_clip);
 
     int32_t num_patches_per_side = image_size / patch_size; // 336 / 14 = 24 - used for embedding-patching boxes (24*24 = 576 patches)
 
@@ -246,12 +258,9 @@ static clip_image_f32 * reshape_by_patch(clip_image_f32 * image, int patch_size)
 
 static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) {
     // std::vector<clip_image_f32*> img_res_v; // format VectN x H x W x RGB (N x 336 x 336 x 3), so interleaved RGB - different to the python implementation which is N x 3 x 336 x 336
-    clip_image_f32_batch img_res_v;
-    img_res_v.size = 0;
-    img_res_v.data = nullptr;
-    if (!clip_image_preprocess(ctx_clip, img, &img_res_v)) {
+    clip_image_f32_batch_ptr img_res_v(clip_image_f32_batch_init());
+    if (!clip_image_preprocess(ctx_clip, img, img_res_v.get())) {
         LOG_ERR("%s: unable to preprocess image\n", __func__);
-        delete[] img_res_v.data;
         return false;
     }
 
@@ -259,66 +268,72 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
 
     const char * mm_patch_merge_type = clip_patch_merge_type(ctx_clip);
 
+    const size_t n_imgs = clip_image_f32_batch_n_images(img_res_v.get());
+
     if (clip_is_minicpmv(ctx_clip) || clip_is_qwen2vl(ctx_clip)) {
         std::vector<float *> image_embd_v;
-        image_embd_v.resize(img_res_v.size);
-        struct clip_image_size * load_image_size = clip_image_size_init();
+        image_embd_v.resize(n_imgs);
+        clip_image_size load_image_size;
 
-        for (size_t i = 0; i < img_res_v.size; i++) {
+        for (size_t i = 0; i < n_imgs; i++) {
             const int64_t t_img_enc_step_start_us = ggml_time_us();
-            image_embd_v[i] = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, img_res_v.data[i].nx, img_res_v.data[i].ny));
-            int patch_size=14;
-            load_image_size->width = img_res_v.data[i].nx;
-            load_image_size->height = img_res_v.data[i].ny;
-            clip_add_load_image_size(ctx_clip, load_image_size);
+            int nx = clip_image_f32_batch_nx(img_res_v.get(), i);
+            int ny = clip_image_f32_batch_ny(img_res_v.get(), i);
+            image_embd_v[i] = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, nx, ny));
+            int patch_size = 14;
+            load_image_size.width = nx;
+            load_image_size.height = ny;
+            clip_add_load_image_size(ctx_clip, &load_image_size);
 
             bool encoded = false;
+            clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), i);
             if (clip_is_qwen2vl(ctx_clip)) {
-                encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]);
+                encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd_v[i]);
             }
             else {
-                encoded = clip_image_encode(ctx_clip, n_threads, reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]);
+                encoded = clip_image_encode(ctx_clip, n_threads, reshape_by_patch(img_res, patch_size), image_embd_v[i]);
             }
 
             if (!encoded) {
-                LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
+                LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) n_imgs);
                 return false;
             }
             const int64_t t_img_enc_steop_batch_us = ggml_time_us();
-            LOG_INF("%s: step %d of %d encoded in %8.2f ms\n", __func__, (int)i+1, (int)img_res_v.size, (t_img_enc_steop_batch_us - t_img_enc_step_start_us) / 1000.0);
+            LOG_INF("%s: step %d of %d encoded in %8.2f ms\n", __func__, (int)i+1, (int)n_imgs, (t_img_enc_steop_batch_us - t_img_enc_step_start_us) / 1000.0);
         }
         const int64_t t_img_enc_batch_us = ggml_time_us();
-        LOG_INF("%s: all %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
+        LOG_INF("%s: all %d segments encoded in %8.2f ms\n", __func__, (int)n_imgs, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
 
         int n_img_pos_out = 0;
         for (size_t i = 0; i < image_embd_v.size(); i++) {
+            int nx = clip_image_f32_batch_nx(img_res_v.get(), i);
+            int ny = clip_image_f32_batch_ny(img_res_v.get(), i);
+            clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), i);
             std::memcpy(
                 image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip),
                 image_embd_v[i],
-                clip_embd_nbytes_by_img(ctx_clip, img_res_v.data[i].nx, img_res_v.data[i].ny));
-            n_img_pos_out += clip_n_patches_by_img(ctx_clip, &img_res_v.data[i]);
+                clip_embd_nbytes_by_img(ctx_clip, nx, ny));
+            n_img_pos_out += clip_n_patches_by_img(ctx_clip, img_res);
         }
         *n_img_pos = n_img_pos_out;
         for (size_t i = 0; i < image_embd_v.size(); i++) {
             free(image_embd_v[i]);
         }
         image_embd_v.clear();
-        load_image_size->width = img->nx;
-        load_image_size->height = img->ny;
-        clip_add_load_image_size(ctx_clip, load_image_size);
-        LOG_INF("%s: load_image_size %d %d\n", __func__, load_image_size->width, load_image_size->height);
-        delete[] img_res_v.data;
-        img_res_v.size = 0;
-        img_res_v.data = nullptr;
+        load_image_size.width = img->nx;
+        load_image_size.height = img->ny;
+        clip_add_load_image_size(ctx_clip, &load_image_size);
+        LOG_INF("%s: load_image_size %d %d\n", __func__, load_image_size.width, load_image_size.height);
     }
     else if (clip_is_glm(ctx_clip)){
         struct clip_image_size * load_image_size = clip_image_size_init();
-        load_image_size->width = img_res_v.data[0].nx;
-        load_image_size->height = img_res_v.data[0].ny;
+        load_image_size->width  = clip_image_f32_batch_nx(img_res_v.get(), 0);
+        load_image_size->height = clip_image_f32_batch_ny(img_res_v.get(), 0);
         clip_add_load_image_size(ctx_clip, load_image_size);
 
-        bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd);
-        int pos = int(load_image_size->width/clip_patch_size(ctx_clip)/2);
+        clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), 0);
+        bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd);
+        int pos = int(load_image_size->width/clip_get_patch_size(ctx_clip)/2);
         *n_img_pos = (pos * pos + 2);
         if (!encoded){
             LOG_ERR("Unable to encode image \n");
@@ -328,8 +343,8 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
     else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
         // flat / default llava-1.5 type embedding
         *n_img_pos = clip_n_patches(ctx_clip);
-        bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); // image_embd shape is 576 x 4096
-        delete[] img_res_v.data;
+        clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), 0);
+        bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd); // image_embd shape is 576 x 4096
         if (!encoded) {
             LOG_ERR("Unable to encode image\n");
 
@@ -340,17 +355,18 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
         // spatial_unpad llava-1.6 type embedding
         // TODO: CLIP needs batching support - in HF the llm projection is separate after encoding, which might be a solution to quickly get batching working
         std::vector<float *> image_embd_v;
-        image_embd_v.resize(img_res_v.size);
-        for (size_t i = 0; i < img_res_v.size; i++) {
+        image_embd_v.resize(n_imgs);
+        for (size_t i = 0; i < n_imgs; i++) {
+            clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), i);
             image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184
-            const bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside
+            const bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside
             if (!encoded) {
-                LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
+                LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) n_imgs);
                 return false;
             }
         }
         const int64_t t_img_enc_batch_us = ggml_time_us();
-        LOG_INF("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
+        LOG_INF("%s: %d segments encoded in %8.2f ms\n", __func__, (int)n_imgs, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
 
         const int32_t * image_grid = clip_image_grid(ctx_clip);
         const size_t num_gridpoints = get_clip_image_grid_size(ctx_clip);
@@ -360,12 +376,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
             grid_pinpoints.push_back({image_grid[i], image_grid[i+1]});
         }
 
-        // free all img_res_v - not needed anymore
-        delete[] img_res_v.data;
-        img_res_v.size = 0;
-        img_res_v.data = nullptr;
-
-        const int32_t image_size = clip_image_size(ctx_clip);
+        const int32_t image_size = clip_get_image_size(ctx_clip);
 
         struct clip_image_grid_shape grid_shape = get_anyres_image_grid_shape({img->nx,img->ny}, grid_pinpoints, image_size);
 

examples/llava/mtmd.cpp

@@ -0,0 +1,341 @@
+#include "clip.h"
+#include "clip-impl.h"
+#include "mtmd.h"
+
+#include "llama.h"
+
+#include <algorithm>
+#include <cerrno>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <limits>
+#include <vector>
+
+struct mtmd_context {
+    struct clip_ctx * ctx_clip;
+    const struct llama_model * text_model;
+    std::vector<float> image_embd_v; // image embedding vector
+    bool print_timings;
+    int n_threads;
+    std::string image_marker;
+
+    // TODO @ngxson : add timings
+
+    mtmd_context(const char * mmproj_fname,
+                   const llama_model * text_model,
+                   const mtmd_context_params & ctx_params) : print_timings(ctx_params.print_timings), n_threads(ctx_params.n_threads), image_marker(ctx_params.image_marker) {
+        clip_context_params ctx_clip_params;
+        ctx_clip_params.use_gpu   = ctx_params.use_gpu;
+        ctx_clip_params.verbosity = ctx_params.verbosity;
+        ctx_clip = clip_init(mmproj_fname, ctx_clip_params);
+        if (!ctx_clip) {
+            throw std::runtime_error(string_format("Failed to load CLIP model from %s\n", mmproj_fname));
+        }
+        this->text_model = text_model;
+    }
+
+    ~mtmd_context() {
+        clip_free(ctx_clip);
+    }
+};
+
+struct mtmd_image_tokens_data {
+    clip_image_f32_batch batch_f32; // preprocessed image patches
+};
+
+struct mtmd_image_tokens {
+    uint32_t nx; // number of tokens in x direction
+    uint32_t ny; // number of tokens in y direction
+    uint32_t n_tokens() const { return nx * ny; }
+    clip_image_f32_batch batch_f32; // preprocessed image patches
+};
+
+mtmd_context * mtmd_init_from_file(const char * mmproj_fname,
+        const struct llama_model * text_model,
+        const struct mtmd_context_params ctx_params) {
+    try {
+        return new mtmd_context(mmproj_fname, text_model, ctx_params);
+    } catch (const std::exception & e) {
+        LOG_ERR("%s: error: %s\n", __func__, e.what());
+        return nullptr;
+    }
+}
+
+void mtmd_free(mtmd_context * ctx) {
+    if (ctx) {
+        delete ctx;
+    }
+}
+
+// copied from common_tokenize
+static std::vector<llama_token> mtmd_tokenize_text_internal(
+    const struct llama_vocab * vocab,
+           const std::string & text,
+                        bool   add_special,
+                        bool   parse_special) {
+    // upper limit for the number of tokens
+    int n_tokens = text.length() + 2 * add_special;
+    std::vector<llama_token> result(n_tokens);
+    n_tokens = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
+    if (n_tokens < 0) {
+        result.resize(-n_tokens);
+        int check = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
+        GGML_ASSERT(check == -n_tokens);
+    } else {
+        result.resize(n_tokens);
+    }
+    return result;
+}
+
+mtmd_input_chunks * mtmd_tokenize(mtmd_context * ctx,
+                                const mtmd_input_text & text,
+                                const std::vector<mtmd_bitmap> & bitmaps) {
+    mtmd_input_chunks * output = new mtmd_input_chunks;
+    auto vocab = llama_model_get_vocab(ctx->text_model);
+
+    std::string prompt_modified(text.text);
+    std::string marker_modified(ctx->image_marker);
+    projector_type proj_type = clip_get_projector_type(ctx->ctx_clip);
+    // a bit hacky here, but works for now
+    // for some models, we need to add prefix and suffix to the image embeddings
+    if (proj_type == PROJECTOR_TYPE_GEMMA3) {
+        // <start_of_image> ... (image embeddings) ... <end_of_image>
+        marker_modified = "<start_of_image>" + ctx->image_marker + "<end_of_image>";
+        string_replace_all(prompt_modified, ctx->image_marker, marker_modified);
+    }
+
+    std::vector<std::string> parts = string_split_str(text.text, ctx->image_marker);
+    output->clear();
+    output->reserve(parts.size());
+
+    size_t i_img = 0;
+
+    for (const auto & part : parts) {
+        //printf("tokenizing part: %s\n", part.c_str());
+        bool add_bos = &parts.front() == &part;
+        auto tokens = mtmd_tokenize_text_internal(vocab, part, text.add_special && add_bos, text.parse_special);
+        if (tokens.empty()) {
+            continue;
+        }
+        mtmd_input_chunk chunk{
+            MTMD_INPUT_CHUNK_TYPE_TEXT,
+            std::move(tokens),
+            {},
+        };
+        output->emplace_back(std::move(chunk));
+
+        if (&parts.back() != &part) {
+            // add image token to middle of 2 parts
+
+            if (i_img >= bitmaps.size()) {
+                LOG_ERR("%s: error: not enough images for %d parts\n", __func__, (int)parts.size());
+                return nullptr;
+            }
+
+            // shim layer
+            clip_image_u8_ptr img_u8(clip_image_u8_init());
+            img_u8->nx = bitmaps[i_img].nx;
+            img_u8->ny = bitmaps[i_img].ny;
+            img_u8->buf.resize(bitmaps[i_img].data.size());
+            std::memcpy(img_u8->buf.data(), bitmaps[i_img].data.data(), img_u8->nx * img_u8->ny * 3);
+
+            // preprocess image
+            clip_image_f32_batch batch_f32;
+            bool ok = clip_image_preprocess(ctx->ctx_clip, img_u8.get(), &batch_f32);
+            if (!ok) {
+                LOG_ERR("Unable to preprocess image\n");
+                return nullptr;
+            }
+
+            mtmd_image_tokens * image_tokens = new mtmd_image_tokens;
+            image_tokens->nx = clip_n_patches(ctx->ctx_clip); // TODO @ngxson : use clip_n_patches_by_image
+            image_tokens->ny = 1; // TODO
+            image_tokens->batch_f32 = std::move(batch_f32);
+
+            mtmd_input_chunk chunk{
+                MTMD_INPUT_CHUNK_TYPE_IMAGE,
+                {},
+                image_tokens,
+            };
+            output->emplace_back(std::move(chunk));
+            i_img++;
+        }
+    }
+
+    return output;
+}
+
+void mtmd_input_chunks_free(mtmd_input_chunks * chunks) {
+    for (auto & chunk : *chunks) {
+        if (chunk.type == MTMD_INPUT_CHUNK_TYPE_IMAGE && chunk.tokens_image) {
+            delete chunk.tokens_image;
+        }
+    }
+    delete chunks;
+}
+
+int32_t mtmd_encode(mtmd_context * ctx, const mtmd_image_tokens * image_tokens) {
+    int n_mmproj_embd = clip_n_mmproj_embd(ctx->ctx_clip);
+    ctx->image_embd_v.resize(image_tokens->n_tokens() * n_mmproj_embd);
+    bool ok = clip_image_batch_encode(
+        ctx->ctx_clip,
+        ctx->n_threads,
+        &image_tokens->batch_f32,
+        ctx->image_embd_v.data());
+    return ok ? 0 : 1;
+}
+
+float * mtmd_get_output_embd(mtmd_context * ctx) {
+    return ctx->image_embd_v.data();
+}
+
+size_t mtmd_helper_get_n_tokens(mtmd_input_chunks * chunks) {
+    size_t n_tokens = 0;
+    for (auto & chunk : *chunks) {
+        if (chunk.type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
+            n_tokens += chunk.tokens_text.size();
+        } else if (chunk.type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
+            n_tokens += chunk.tokens_image->n_tokens();
+        } else {
+            GGML_ASSERT(false && "chunk type not supported");
+        }
+    }
+    return n_tokens;
+}
+
+// helper struct to make working with embd batch easier
+// note: this will be removed after llama_batch_ext refactoring
+struct decode_embd_batch {
+    std::vector<llama_pos>      pos;
+    std::vector<int32_t>        n_seq_id;
+    std::vector<llama_seq_id>   seq_id_0;
+    std::vector<llama_seq_id *> seq_ids;
+    std::vector<int8_t>         logits;
+    llama_batch batch;
+    decode_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
+        pos     .resize(n_tokens);
+        n_seq_id.resize(n_tokens);
+        seq_ids .resize(n_tokens + 1);
+        logits  .resize(n_tokens);
+        seq_id_0.resize(1);
+        seq_id_0[0] = seq_id;
+        seq_ids [n_tokens] = nullptr;
+        batch = {
+            /*n_tokens       =*/ n_tokens,
+            /*tokens         =*/ nullptr,
+            /*embd           =*/ embd,
+            /*pos            =*/ pos.data(),
+            /*n_seq_id       =*/ n_seq_id.data(),
+            /*seq_id         =*/ seq_ids.data(),
+            /*logits         =*/ logits.data(),
+        };
+        for (int i = 0; i < n_tokens; i++) {
+            batch.pos     [i] = pos_0 + i;
+            batch.n_seq_id[i] = 1;
+            batch.seq_id  [i] = seq_id_0.data();
+            batch.logits  [i] = false;
+        }
+    }
+};
+
+int32_t mtmd_helper_eval(mtmd_context * ctx,
+        llama_context * lctx,
+        mtmd_input_chunks * chunks,
+        llama_pos pos0,
+        llama_seq_id seq_id,
+        int32_t n_batch) {
+    int32_t ret;
+    llama_pos n_past = pos0;
+    llama_batch text_batch = llama_batch_init(n_batch, 0, 1);
+
+    for (auto & chunk : *chunks) {
+        bool is_last = &chunk == &chunks->back();
+        if (chunk.type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
+            // TODO @ngxson : may need to split into smaller batches
+            text_batch.n_tokens = chunk.tokens_text.size();
+            for (size_t i = 0; i < chunk.tokens_text.size(); i++) {
+                text_batch.token   [i]    = chunk.tokens_text[i];
+                text_batch.pos     [i]    = n_past++;
+                text_batch.n_seq_id[i]    = 1;
+                text_batch.seq_id  [i][0] = seq_id;
+                text_batch.logits  [i]    = false;
+            }
+            if (is_last) {
+                // always get logits for last input chunk
+                text_batch.logits[text_batch.n_tokens - 1] = true;
+            }
+            ret = llama_decode(lctx, text_batch);
+            if (ret != 0) {
+                LOG_ERR("failed to decode text\n");
+                llama_batch_free(text_batch);
+                return ret;
+            }
+
+        } else if (chunk.type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
+            GGML_ASSERT(!is_last && "logits for last image chunk is not yet support");
+            GGML_ASSERT(chunk.tokens_image != nullptr);
+            int64_t t0 = ggml_time_ms();
+            if (ctx->print_timings) {
+                LOG_INF("encoding image...\n");
+            }
+            ret = mtmd_encode(ctx, chunk.tokens_image);
+            if (ret != 0) {
+                LOG_ERR("failed to encode image\n");
+                llama_batch_free(text_batch);
+                return ret;
+            }
+            if (ctx->print_timings) {
+                LOG_INF("image encoded in %" PRId64 " ms\n", ggml_time_ms() - t0);
+            }
+
+            int32_t n_tokens = chunk.tokens_image->n_tokens();
+            float * embd = mtmd_get_output_embd(ctx);
+            decode_embd_batch batch_img(embd, n_tokens, n_past, 0);
+            int64_t t1 = ggml_time_ms();
+            ret = llama_decode(lctx, batch_img.batch);
+            if (ret != 0) {
+                LOG_ERR("failed to decode image\n");
+                llama_batch_free(text_batch);
+                return ret;
+            }
+            if (ctx->print_timings) {
+                LOG_INF("image decoded in %" PRId64 " ms\n", ggml_time_ms() - t1);
+            }
+
+            n_past += n_tokens;
+
+        } else {
+            GGML_ASSERT(false && "chunk type not supported");
+        }
+    }
+
+    llama_batch_free(text_batch);
+    return 0;
+}
+
+int32_t mtmd_helper_bitmap_init_from_buf(const unsigned char * buf, size_t len, mtmd_bitmap & output) {
+    clip_image_u8_ptr img_u8(clip_image_u8_init());
+    bool ok = clip_image_load_from_bytes(buf, len, img_u8.get());
+    if (!ok) {
+        LOG_ERR("Unable to load image from buffer\n");
+        return 1;
+    }
+    unsigned char * data = clip_image_u8_get_data(img_u8.get(), &output.nx, &output.ny);
+    output.data.resize(output.nx * output.ny * 3);
+    std::memcpy(output.data.data(), data, output.nx * output.ny * 3);
+    return 0;
+}
+
+int32_t mtmd_helper_bitmap_init_from_file(const char * fname, mtmd_bitmap & output) {
+    clip_image_u8_ptr img_u8(clip_image_u8_init());
+    bool ok = clip_image_load_from_file(fname, img_u8.get());
+    if (!ok) {
+        LOG_ERR("Unable to load image %s\n", fname);
+        return 1;
+    }
+    unsigned char * data = clip_image_u8_get_data(img_u8.get(), &output.nx, &output.ny);
+    output.data.resize(output.nx * output.ny * 3);
+    std::memcpy(output.data.data(), data, output.nx * output.ny * 3);
+    return 0;
+}

examples/llava/mtmd.h

@@ -0,0 +1,146 @@
+#ifndef MTMD_H
+#define MTMD_H
+
+#include "ggml.h"
+#include "llama.h"
+#include "clip.h"
+
+#include <vector>
+#include <cinttypes>
+#include <memory>
+
+#ifdef LLAMA_SHARED
+#    if defined(_WIN32) && !defined(__MINGW32__)
+#        ifdef LLAMA_BUILD
+#            define MTMD_API __declspec(dllexport)
+#        else
+#            define MTMD_API __declspec(dllimport)
+#        endif
+#    else
+#        define MTMD_API __attribute__ ((visibility ("default")))
+#    endif
+#else
+#    define MTMD_API
+#endif
+
+#ifdef __cplusplus
+
+enum mtmd_input_chunk_type {
+    MTMD_INPUT_CHUNK_TYPE_TEXT,
+    MTMD_INPUT_CHUNK_TYPE_IMAGE,
+};
+
+struct mtmd_context;
+struct mtmd_image_tokens;
+
+// represents raw image data, layout is RGBRGBRGB...
+// length of data must be nx * ny * 3
+struct mtmd_bitmap {
+    uint32_t nx;
+    uint32_t ny;
+    std::vector<unsigned char> data;
+};
+
+struct mtmd_input_chunk {
+    mtmd_input_chunk_type type;
+    std::vector<llama_token> tokens_text;
+    mtmd_image_tokens * tokens_image = nullptr;
+};
+
+using mtmd_input_chunks = std::vector<mtmd_input_chunk>;
+
+struct mtmd_context_params {
+    bool use_gpu = true;
+    bool print_timings = true;
+    int n_threads = 4;
+    enum ggml_log_level verbosity = GGML_LOG_LEVEL_INFO;
+    const char * image_marker = "<__image__>";
+};
+
+struct mtmd_input_text {
+    std::string text;
+    bool add_special;
+    bool parse_special;
+};
+
+// initialize the mtmd context
+// return nullptr on failure
+MTMD_API mtmd_context * mtmd_init_from_file(const char * mmproj_fname,
+                                                const llama_model * text_model,
+                                                const mtmd_context_params ctx_params);
+
+MTMD_API void mtmd_free(mtmd_context * ctx);
+
+// tokenize an input text prompt and an image
+// the prompt must have the input image marker (default: "<__image__>") in it
+// the marker will be replaced with the image tokens
+// for example:
+//   "here is an image: <__image__>\ndescribe it in detail."
+//   this will gives 3 chunks:
+//   1. "here is an image: <start_of_image>"
+//   2. (image tokens)
+//   3. "<end_of_image>\ndescribe it in detail."
+// number of bitmaps must be equal to the number of image markers in the prompt
+// this function is thread-safe (shared ctx)
+MTMD_API mtmd_input_chunks * mtmd_tokenize(mtmd_context * ctx,
+                                const mtmd_input_text & text,
+                                const std::vector<mtmd_bitmap> & bitmaps);
+
+// free image chunk data
+MTMD_API void mtmd_input_chunks_free(mtmd_input_chunks * chunks);
+
+// returns 0 on success
+MTMD_API int32_t mtmd_encode(mtmd_context * ctx,
+                            const mtmd_image_tokens * image_tokens);
+
+// get output embeddings from the last encode pass
+MTMD_API float * mtmd_get_output_embd(mtmd_context * ctx);
+
+//
+// helper functions (can be implemented based on other functions)
+//
+
+// helper to count the total number of tokens from a list of chunks, useful to keep track of n_past
+MTMD_API size_t mtmd_helper_get_n_tokens(mtmd_input_chunks * chunks);
+
+// helper function that automatically:
+// 1. run llama_decode() on text chunks
+// 2. run mtmd_encode() on image chunks, then mtmd_get_output_embd() and then llama_decode()
+// if any of the mtmd_encode() or llama_decode() calls return non-zero, stop and forward the error
+// otherwise, returns 0 on success
+MTMD_API int32_t mtmd_helper_eval(mtmd_context * ctx,
+                                llama_context * lctx,
+                                mtmd_input_chunks * chunks,
+                                llama_pos pos0,
+                                llama_seq_id seq_id,
+                                int32_t n_batch);
+
+// helper function to construct a mtmd_bitmap from a file
+// returns 0 on success
+// this function is thread-safe
+MTMD_API int32_t mtmd_helper_bitmap_init_from_file(const char * fname, mtmd_bitmap & output);
+
+// helper function to construct a mtmd_bitmap from a buffer
+// the buffer must be an image in format supported by stb_image (jpg, png, bmp, gif, etc.)
+// returns 0 on success
+// this function is thread-safe
+MTMD_API int32_t mtmd_helper_bitmap_init_from_buf(const unsigned char * buf, size_t len, mtmd_bitmap & output);
+
+// convenient unique_ptr wrappers
+struct mtmd_context_deleter {
+    void operator()(mtmd_context * val) { mtmd_free(val); }
+};
+using mtmd_context_ptr = std::unique_ptr<mtmd_context, mtmd_context_deleter>;
+
+struct mtmd_input_chunks_deleter {
+    void operator()(mtmd_input_chunks * val) { mtmd_input_chunks_free(val); }
+};
+using mtmd_input_chunks_ptr = std::unique_ptr<mtmd_input_chunks, mtmd_input_chunks_deleter>;
+
+#else
+
+static_assert(false && "C header is not yet supported by this library");
+
+#endif
+
+#endif

examples/quantize/quantize.cpp

@@ -9,6 +9,7 @@
 #include <fstream>
 #include <cmath>
 #include <cctype>
+#include <algorithm>
 
 struct quant_option {
     std::string name;
@@ -16,7 +17,7 @@ struct quant_option {
     std::string desc;
 };
 
-static const std::vector<struct quant_option> QUANT_OPTIONS = {
+static const std::vector<quant_option> QUANT_OPTIONS = {
     { "Q4_0",     LLAMA_FTYPE_MOSTLY_Q4_0,     " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
     { "Q4_1",     LLAMA_FTYPE_MOSTLY_Q4_1,     " 4.78G, +0.4511 ppl @ Llama-3-8B",  },
     { "Q5_0",     LLAMA_FTYPE_MOSTLY_Q5_0,     " 5.21G, +0.1316 ppl @ Llama-3-8B",  },
@@ -105,7 +106,8 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
 //
 [[noreturn]]
 static void usage(const char * executable) {
-    printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
+    printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type]\n", executable);
+    printf("       [--token-embedding-type] [--tensor-type] [--keep-split] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n");
     printf("  --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
     printf("  --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
     printf("  --pure: Disable k-quant mixtures and quantize all tensors to the same type\n");
@@ -114,6 +116,8 @@ static void usage(const char * executable) {
     printf("  --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n");
     printf("  --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n");
     printf("  --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n");
+    printf("  --tensor-type TENSOR=TYPE: quantize this tensor to this ggml_type. example: --tensor-type attn_q=q8_0\n");
+    printf("      Advanced option to selectively quantize tensors. May be specified multiple times.\n");
     printf("  --keep-split: will generate quantized model in the same shards as input\n");
     printf("  --override-kv KEY=TYPE:VALUE\n");
     printf("      Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n");
@@ -244,6 +248,107 @@ static ggml_type parse_ggml_type(const char * arg) {
     return GGML_TYPE_COUNT;
 }
 
+// Allowed tensors for arbitrary quantization with --tensor-type option
+static const std::vector<std::string> ALLOWED_TENSOR_TYPE = {
+    "attn_k",
+    "attn_kv_a_mqa",
+    "attn_kv_b",
+    "attn_o",
+    "attn_output",
+    "attn_q",
+    "attn_q_a",
+    "attn_q_b",
+    "attn_qkv",
+    "attn_v",
+    "channel_mix_key",
+    "channel_mix_receptance",
+    "channel_mix_value",
+    "cls",
+    "cls.output",
+    "cross_attn_k",
+    "cross_attn_o",
+    "cross_attn_q",
+    "cross_attn_v",
+    "ffn_act",
+    "ffn_down",
+    "ffn_down_exps",
+    "ffn_down_shexp",
+    "ffn_gate",
+    "ffn_gate_exps",
+    "ffn_gate_shexp",
+    "ffn_up",
+    "ffn_up_exps",
+    "ffn_up_shexp",
+    "ssm_in",
+    "ssm_out",
+    "time_mix_gate",
+    "time_mix_key",
+    "time_mix_output",
+    "time_mix_receptance",
+    "time_mix_value",
+};
+
+// changes to this struct must be replicated in llama-quant.cpp
+struct tensor_quantization {
+    std::string name;
+    ggml_type quant = GGML_TYPE_COUNT;
+};
+
+static bool parse_tensor_type(const char * data, std::vector<tensor_quantization> & tensor_type) {
+    const char * sep = strchr(data, '=');
+    if (sep == nullptr) {
+        printf("\n%s: malformed tensor type '%s'\n\n", __func__, data);
+        return false;
+    }
+
+    const size_t tn_len = sep - data;
+    if (tn_len == 0) {
+        printf("\n%s: missing tensor name\n\n", __func__);
+        return false;
+    }
+
+    if (const size_t qt_len = strlen(sep); qt_len == 1) {
+        printf("\n%s: missing quantization type\n\n", __func__);
+        return false;
+    }
+
+    std::string tn(data, tn_len);
+    std::transform(tn.begin(), tn.end(), tn.begin(), tolower);
+    sep++;
+    const std::string qt(sep);
+
+    bool found = false;
+    for (const auto & allowed : ALLOWED_TENSOR_TYPE) {
+        std::string tensor;
+        tensor = tn.rfind('.') != std::string::npos ? tn.substr(tn.rfind('.') + 1) : tn;
+        // handle special case of cls.output
+        std::string cls_output = "cls.output";
+        if (tn.find(cls_output) != std::string::npos) {
+            tensor = "cls.output";
+        }
+        // check if an allowed tensor exists and it's at the end of the kv string
+        if (tensor == allowed) {
+            found = true;
+            break;
+        }
+    }
+    if (!found) {
+        printf("\n%s: invalid tensor name '%s'\n\n", __func__, tn.c_str());
+        return false;
+    }
+
+    if (parse_ggml_type(qt.c_str()) == GGML_TYPE_COUNT) {
+        printf("\n%s: invalid quantization type '%s'\n\n", __func__, qt.c_str());
+        return false;
+    }
+
+    tensor_quantization tqz;
+    tqz.name = tn;
+    tqz.quant = parse_ggml_type(qt.c_str());
+    tensor_type.emplace_back(std::move(tqz));
+    return true;
+}
+
 int main(int argc, char ** argv) {
     if (argc < 3) {
         usage(argv[0]);
@@ -255,6 +360,7 @@ int main(int argc, char ** argv) {
     std::string imatrix_file;
     std::vector<std::string> included_weights, excluded_weights;
     std::vector<llama_model_kv_override> kv_overrides;
+    std::vector<tensor_quantization> tensor_types;
 
     for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
         if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) {
@@ -277,6 +383,10 @@ int main(int argc, char ** argv) {
             } else {
                 usage(argv[0]);
             }
+        } else if (strcmp(argv[arg_idx], "--tensor-type") == 0) {
+            if (arg_idx == argc-1 || !parse_tensor_type(argv[++arg_idx], tensor_types)) {
+                usage(argv[0]);
+            }
         } else if (strcmp(argv[arg_idx], "--override-kv") == 0) {
             if (arg_idx == argc-1 || !string_parse_kv_override(argv[++arg_idx], kv_overrides)) {
                 usage(argv[0]);
@@ -361,6 +471,9 @@ int main(int argc, char ** argv) {
         kv_overrides.back().key[0] = 0;
         params.kv_overrides = &kv_overrides;
     }
+    if (!tensor_types.empty()) {
+        params.tensor_types = &tensor_types;
+    }
 
     llama_backend_init();
 

examples/rpc/rpc-server.cpp

@@ -126,7 +126,7 @@ static std::string fs_get_cache_directory() {
     if (getenv("LLAMA_CACHE")) {
         cache_directory = std::getenv("LLAMA_CACHE");
     } else {
-#ifdef __linux__
+#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX)
         if (std::getenv("XDG_CACHE_HOME")) {
             cache_directory = std::getenv("XDG_CACHE_HOME");
         } else {
@@ -136,7 +136,9 @@ static std::string fs_get_cache_directory() {
         cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
 #elif defined(_WIN32)
         cache_directory = std::getenv("LOCALAPPDATA");
-#endif // __linux__
+#else
+#  error Unknown architecture
+#endif
         cache_directory = ensure_trailing_slash(cache_directory);
         cache_directory += "llama.cpp";
     }

examples/run/run.cpp

@@ -697,8 +697,10 @@ class LlamaData {
         std::vector<std::string> headers = { "User-Agent: llama-cpp", "Accept: application/json" };
         std::string              url;
 
+        std::string model_endpoint = get_model_endpoint();
+
         if (pos == std::string::npos) {
-            auto [model_name, manifest_url] = extract_model_and_tag(model, "https://huggingface.co/v2/");
+            auto [model_name, manifest_url] = extract_model_and_tag(model, model_endpoint + "v2/");
             hfr                             = model_name;
 
             nlohmann::json manifest;
@@ -713,7 +715,7 @@ class LlamaData {
             hff = model.substr(pos + 1);
         }
 
-        url = "https://huggingface.co/" + hfr + "/resolve/main/" + hff;
+        url = model_endpoint + hfr + "/resolve/main/" + hff;
 
         return download(url, bn, true, headers);
     }

examples/server/server.cpp

@@ -3907,6 +3907,21 @@ int main(int argc, char ** argv) {
         res_ok(res, {{ "success", true }});
     };
 
+    const auto handle_api_show = [&ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) {
+        json data = {
+            {
+                "template", common_chat_templates_source(ctx_server.chat_templates.get()),
+            },
+            {
+                "model_info", {
+                    { "llama.context_length", ctx_server.slots.back().n_ctx, },
+                }
+            },
+        };
+
+        res_ok(res, data);
+    };
+
     // handle completion-like requests (completion, chat, infill)
     // we can optionally provide a custom format for partial results and final results
     const auto handle_completions_impl = [&ctx_server, &res_error, &res_ok](
@@ -4471,6 +4486,7 @@ int main(int argc, char ** argv) {
     svr->Get ("/metrics",             handle_metrics);
     svr->Get ("/props",               handle_props);
     svr->Post("/props",               handle_props_change);
+    svr->Post("/api/show",            handle_api_show);
     svr->Get ("/models",              handle_models); // public endpoint (no API key check)
     svr->Get ("/v1/models",           handle_models); // public endpoint (no API key check)
     svr->Post("/completion",          handle_completions); // legacy

examples/sycl/build.sh

@@ -8,10 +8,10 @@ cd build
 source /opt/intel/oneapi/setvars.sh
 
 #for FP16
-#cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON # faster for long-prompt inference
+#cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON -DLLAMA_CURL=OFF # faster for long-prompt inference
 
 #for FP32
-cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
+cmake .. -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=OFF
 
 #build example/main
 #cmake --build . --config Release --target main

ggml/CMakeLists.txt

@@ -170,7 +170,6 @@ option(GGML_HIP                             "ggml: use HIP"
 option(GGML_HIP_GRAPHS                      "ggml: use HIP graph, experimental, slow"         OFF)
 option(GGML_HIP_NO_VMM                      "ggml: do not try to use HIP VMM"                 ON)
 option(GGML_HIP_ROCWMMA_FATTN               "ggml: enable rocWMMA for FlashAttention"         OFF)
-option(GGML_HIP_UMA                         "ggml: use HIP unified memory architecture"       OFF)
 option(GGML_VULKAN                          "ggml: use Vulkan"                                OFF)
 option(GGML_VULKAN_CHECK_RESULTS            "ggml: run Vulkan op checks"                      OFF)
 option(GGML_VULKAN_DEBUG                    "ggml: enable Vulkan debug output"                OFF)

ggml/include/ggml.h

@@ -507,17 +507,12 @@ extern "C" {
 
         GGML_OP_UNARY,
 
-        GGML_OP_MAP_UNARY,
-        GGML_OP_MAP_BINARY,
-
-        GGML_OP_MAP_CUSTOM1_F32,
-        GGML_OP_MAP_CUSTOM2_F32,
-        GGML_OP_MAP_CUSTOM3_F32,
-
         GGML_OP_MAP_CUSTOM1,
         GGML_OP_MAP_CUSTOM2,
         GGML_OP_MAP_CUSTOM3,
 
+        GGML_OP_CUSTOM,
+
         GGML_OP_CROSS_ENTROPY_LOSS,
         GGML_OP_CROSS_ENTROPY_LOSS_BACK,
         GGML_OP_OPT_STEP_ADAMW,
@@ -1722,24 +1717,29 @@ extern "C" {
             float                 p0,
             float                 p1);
 
-    // nearest interpolate
+    enum ggml_scale_mode {
+        GGML_SCALE_MODE_NEAREST  = 0,
+        GGML_SCALE_MODE_BILINEAR = 1,
+    };
+
+    // interpolate
     // multiplies ne0 and ne1 by scale factor
-    // used in stable-diffusion
     GGML_API struct ggml_tensor * ggml_upscale(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
-            int                   scale_factor);
+            int                   scale_factor,
+            enum ggml_scale_mode  mode);
 
-    // nearest interpolate
-    // nearest interpolate to specified dimensions
-    // used in tortoise.cpp
+    // interpolate
+    // interpolate scale to specified dimensions
     GGML_API struct ggml_tensor * ggml_upscale_ext(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
             int                   ne0,
             int                   ne1,
             int                   ne2,
-            int                   ne3);
+            int                   ne3,
+            enum ggml_scale_mode  mode);
 
     // pad each dimension with zeros: [x, ..., x] -> [x, ..., x, 0, ..., 0]
     GGML_API struct ggml_tensor * ggml_pad(
@@ -1916,83 +1916,6 @@ extern "C" {
 
     // custom operators
 
-    typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
-    typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
-
-    typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
-    typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
-    typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
-
-    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_f32(
-            struct ggml_context        * ctx,
-            struct ggml_tensor         * a,
-                   ggml_unary_op_f32_t   fun),
-        "use ggml_map_custom1 instead");
-
-    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
-            struct ggml_context        * ctx,
-            struct ggml_tensor         * a,
-                   ggml_unary_op_f32_t   fun),
-        "use ggml_map_custom1_inplace instead");
-
-    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_f32(
-            struct ggml_context         * ctx,
-            struct ggml_tensor          * a,
-            struct ggml_tensor          * b,
-                   ggml_binary_op_f32_t   fun),
-        "use ggml_map_custom2 instead");
-
-    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
-            struct ggml_context         * ctx,
-            struct ggml_tensor          * a,
-            struct ggml_tensor          * b,
-                   ggml_binary_op_f32_t   fun),
-        "use ggml_map_custom2_inplace instead");
-
-    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_f32(
-            struct ggml_context          * ctx,
-            struct ggml_tensor           * a,
-                   ggml_custom1_op_f32_t   fun),
-        "use ggml_map_custom1 instead");
-
-    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
-            struct ggml_context          * ctx,
-            struct ggml_tensor           * a,
-                   ggml_custom1_op_f32_t   fun),
-        "use ggml_map_custom1_inplace instead");
-
-    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_f32(
-            struct ggml_context          * ctx,
-            struct ggml_tensor           * a,
-            struct ggml_tensor           * b,
-                   ggml_custom2_op_f32_t   fun),
-        "use ggml_map_custom2 instead");
-
-    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
-            struct ggml_context          * ctx,
-            struct ggml_tensor           * a,
-            struct ggml_tensor           * b,
-                   ggml_custom2_op_f32_t   fun),
-        "use ggml_map_custom2_inplace instead");
-
-    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_f32(
-            struct ggml_context          * ctx,
-            struct ggml_tensor           * a,
-            struct ggml_tensor           * b,
-            struct ggml_tensor           * c,
-                   ggml_custom3_op_f32_t   fun),
-        "use ggml_map_custom3 instead");
-
-    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
-            struct ggml_context          * ctx,
-            struct ggml_tensor           * a,
-            struct ggml_tensor           * b,
-            struct ggml_tensor           * c,
-                   ggml_custom3_op_f32_t   fun),
-        "use ggml_map_custom3_inplace instead");
-
-    // custom operators v2
-
     typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata);
     typedef void (*ggml_custom2_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, int ith, int nth, void * userdata);
     typedef void (*ggml_custom3_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, const struct ggml_tensor * c, int ith, int nth, void * userdata);
@@ -2048,6 +1971,30 @@ extern "C" {
             int                     n_tasks,
             void                  * userdata);
 
+    typedef void (*ggml_custom_op_t)(struct ggml_tensor * dst , int ith, int nth, void * userdata);
+
+    GGML_API struct ggml_tensor * ggml_custom_4d(
+            struct ggml_context * ctx,
+            enum ggml_type        type,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2,
+            int64_t               ne3,
+            struct ggml_tensor ** args,
+            int                   n_args,
+            ggml_custom_op_t      fun,
+            int                   n_tasks,
+            void                * userdata);
+
+    GGML_API struct ggml_tensor * ggml_custom_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor ** args,
+            int                   n_args,
+            ggml_custom_op_t      fun,
+            int                   n_tasks,
+            void                * userdata);
+
     // loss function
 
     GGML_API struct ggml_tensor * ggml_cross_entropy_loss(

ggml/src/ggml-cann/acl_tensor.cpp

@@ -41,6 +41,8 @@ aclDataType ggml_cann_type_mapping(ggml_type type) {
             return ACL_INT4;
         case GGML_TYPE_Q8_0:
             return ACL_INT8;
+        case GGML_TYPE_I64:
+            return ACL_INT64;
         default:
             return ACL_DT_UNDEFINED;
     }

ggml/src/ggml-cann/aclnn_ops.cpp

@@ -59,6 +59,12 @@
 #include <aclnnop/aclnn_div.h>
 #include <aclnnop/aclnn_convolution.h>
 #include <aclnnop/aclnn_elu.h>
+#include <aclnnop/aclnn_log.h>
+#include <aclnnop/aclnn_mean.h>
+#include <aclnnop/aclnn_reflection_pad1d.h>
+#include <aclnnop/aclnn_eq_tensor.h>
+#include <aclnnop/aclnn_gt_scalar.h>
+#include <aclnnop/aclnn_pow.h>
 #include <float.h>
 
 #include <cmath>
@@ -139,23 +145,6 @@ static void aclnn_cast(ggml_backend_cann_context& ctx, aclTensor* acl_src,
     GGML_CANN_CALL_ACLNN_OP(Cast, acl_src, cast_data_type, acl_dst);
 }
 
-/**
- * @brief Casts the elements of a tensor to a specified data type using the CANN backend.
- *
- * @details This function performs a type conversion on the elements of the input tensor `acl_src`
- *          and stores the results in the destination tensor `acl_dst`. The conversion type is
- *          determined based on the `dst` tensor's data type.
- *
- * @param ctx The context for the CANN backend operations.
- * @param acl_src The source tensor whose elements will be cast.
- * @param acl_dst The destination tensor that will store the casted elements.
- * @param dst The ggml tensor specifying the target data type.
- */
-static void aclnn_cast(ggml_backend_cann_context& ctx, aclTensor* acl_src,
-                    aclTensor* acl_dst, ggml_tensor* dst) {
-    aclnn_cast(ctx, acl_src, acl_dst, ggml_cann_type_mapping(dst->type));
-}
-
 void ggml_cann_repeat(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     ggml_tensor* src = dst->src[0];
     GGML_ASSERT(ggml_can_repeat(src, dst));
@@ -636,6 +625,10 @@ static void ggml_cann_avg_pool2d(ggml_backend_cann_context& ctx,
     bool count_include_pad = true;
     int64_t divisor_override = 0;
     int8_t cube_math_type = 0;
+#ifdef ASCEND_310P
+    cube_math_type = 1;
+#endif
+
     GGML_CANN_CALL_ACLNN_OP(AvgPool2d, acl_src, kernel_size, strides, paddings_avg,
                     ceil_mode, count_include_pad, divisor_override,
                     cube_math_type, acl_dst);
@@ -762,7 +755,7 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
         if (dst->type == src0->type) {
             cann_copy(ctx, acl_src, acl_dst);
         } else {
-            aclnn_cast(ctx, acl_src, acl_dst, dst);
+            aclnn_cast(ctx, acl_src, acl_dst, ggml_cann_type_mapping(dst->type));
         }
     } else {
         if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) {
@@ -787,7 +780,7 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
                     ggml_type_size(dst->type), src0->ne, src_trans_nb,
                     GGML_MAX_DIMS);
 
-                aclnn_cast(ctx, acl_src, src_trans_tensor, dst);
+                aclnn_cast(ctx, acl_src, src_trans_tensor, ggml_cann_type_mapping(dst->type));
                 size_t cpy_size = ggml_nbytes(dst);
                 ACL_CHECK(aclrtMemcpyAsync(
                     dst->data, cpy_size, src_trans_buffer, cpy_size,
@@ -809,7 +802,7 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
                 ggml_type_size(dst->type), src0->ne, src_trans_nb,
                 GGML_MAX_DIMS);
 
-            aclnn_cast(ctx, acl_src, src_trans_tensor, dst);
+            aclnn_cast(ctx, acl_src, src_trans_tensor, ggml_cann_type_mapping(dst->type));
 
             size_t cpy_size = ggml_nbytes(dst);
             ACL_CHECK(aclrtMemcpyAsync(dst->data, cpy_size, src_trans_buffer,
@@ -1153,7 +1146,7 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
             tmp_cast_buffer, ggml_cann_type_mapping(dst->type),
             ggml_type_size(dst->type), tmp_im2col_ne, temp_cast_nb,
             GGML_MAX_DIMS - 1, ACL_FORMAT_ND);
-        aclnn_cast(ctx, tmp_im2col_tensor, tmp_cast_tensor, dst);
+        aclnn_cast(ctx, tmp_im2col_tensor, tmp_cast_tensor, ggml_cann_type_mapping(dst->type));
     }
 
     // post-processing
@@ -1728,7 +1721,7 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
             aclTensor* src_trans_tensor = ggml_cann_create_tensor(
                 src_trans_buffer, ACL_FLOAT, ggml_type_size(dst->type),
                 src0->ne, src_trans_nb, GGML_MAX_DIMS);
-            aclnn_cast(ctx, acl_src0, src_trans_tensor, dst);
+            aclnn_cast(ctx, acl_src0, src_trans_tensor, ggml_cann_type_mapping(dst->type));
             aclnn_embedding_4d(ctx, src_trans_buffer, src0->ne,
                                    src_trans_nb, src1, dst);
             ACL_CHECK(aclDestroyTensor(acl_src0));
@@ -1778,7 +1771,7 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
                 src0->data, ACL_INT8, sizeof(int8_t), weight_ne, weight_nb,
                 GGML_MAX_DIMS + 1);
             aclTensor* acl_scale_tensor = ggml_cann_create_tensor(
-                src0->data, ACL_FLOAT16, sizeof(float16_t), scale_ne, scale_nb,
+                src0->data, ACL_FLOAT16, sizeof(uint16_t), scale_ne, scale_nb,
                 GGML_MAX_DIMS + 1, ACL_FORMAT_ND, scale_offset);
             aclTensor* dequant_tensor = ggml_cann_create_tensor(
                 dequant_buffer_allocator.get(), ACL_FLOAT, sizeof(float_t),
@@ -2069,7 +2062,7 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx,
             output_buffer, ACL_FLOAT16, output_elem_size, output_cast_ne,
             output_cast_nb, GGML_MAX_DIMS);
         aclTensor* acl_dst_tensor = ggml_cann_create_tensor(dst);
-        aclnn_cast(ctx, acl_output_tensor, acl_dst_tensor, dst);
+        aclnn_cast(ctx, acl_output_tensor, acl_dst_tensor, ggml_cann_type_mapping(dst->type));
 
         ACL_CHECK(aclDestroyTensor(acl_output_tensor));
         ACL_CHECK(aclDestroyTensor(acl_dst_tensor));
@@ -2154,37 +2147,29 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
     ggml_tensor* src1 = dst->src[1];  // position
     ggml_tensor* src2 = dst->src[2];  // freq_factors
 
-    // arange, [0,1,...,ne0/2]
-    int64_t arange_length = src0->ne[0] / 2;
-    ggml_cann_pool_alloc arange_allocator(ctx.pool(),
-                                          arange_length * sizeof(float_t));
-    void* arange_buffer = arange_allocator.get();
-    int64_t arange_ne[] = {arange_length, 1, 1, 1};
-    size_t arange_nb[] = {sizeof(float_t), sizeof(float_t), sizeof(float_t),
-                          arange_length * sizeof(float_t)};
+    GGML_TENSOR_BINARY_OP_LOCALS
 
-    aclTensor* acl_arange_tensor =
-        ggml_cann_create_tensor(arange_buffer, ACL_FLOAT, sizeof(float_t),
-                                arange_ne, arange_nb, GGML_MAX_DIMS);
+    // theta_scale arange, [0,1,...,ne00/2 - 1]
+    int64_t theta_scale_length = ne00 / 2;
+    ggml_cann_pool_alloc theta_scale_allocator(ctx.pool(),
+                                          theta_scale_length * sizeof(float_t));
+    void* theta_scale_buffer = theta_scale_allocator.get();
+    int64_t theta_scale_ne[] = {theta_scale_length, 1, 1, 1};
+    size_t theta_scale_nb[] = {sizeof(float_t), sizeof(float_t), sizeof(float_t),
+                          theta_scale_length * sizeof(float_t)};
+
+    aclTensor* acl_theta_scale_tensor =
+        ggml_cann_create_tensor(theta_scale_buffer, ACL_FLOAT, sizeof(float_t),
+                                theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
     float start = 0;
     float step = 1;
-    float stop = src0->ne[0] / 2;
-    float n_elements = src0->ne[0] / 2;
-    aclnn_arange(ctx, acl_arange_tensor, start, stop, step, n_elements);
+    float stop = ne00 / 2;
+    float n_elements = ne00 / 2;
+    aclnn_arange(ctx, acl_theta_scale_tensor, start, stop, step, n_elements);
 
     // power
-    // aclnnPowScalarTensor(): @param self is tensor which should be scalar, so
-    // use aclnn_pow_tensor_tensor() until fixed. aclScalar* acl_theta_scale =
-    // aclCreateScalar(&theta_scale, aclDataType::ACL_FLOAT);
-    // aclnn_power_scalar_tensor(ctx, acl_theta_scale, acl_arange_tensor,
-    // acl_power_tensor);
-    ggml_cann_pool_alloc theta_scale_allocator(ctx.pool(),
-                                               arange_length * sizeof(float_t));
-    void* theta_scale_buffer = theta_scale_allocator.get();
-    aclTensor* acl_theta_scale_tensor = aclnn_values(
-        ctx, theta_scale_buffer, arange_length * sizeof(float_t), arange_ne,
-        GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), theta_scale);
-    aclnn_pow_tensor_tensor(ctx, acl_theta_scale_tensor, acl_arange_tensor);
+    aclScalar* acl_theta_scale = aclCreateScalar(&theta_scale, aclDataType::ACL_FLOAT);
+    GGML_CANN_CALL_ACLNN_OP(PowScalarTensor, acl_theta_scale, acl_theta_scale_tensor, acl_theta_scale_tensor);
 
     // freq_scale
     if (freq_scale != 1) {
@@ -2195,7 +2180,7 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
     if (src2) {
         aclTensor* acl_freq_factors_tensor = ggml_cann_create_tensor(
             src2->data, ggml_cann_type_mapping(src2->type),
-            ggml_type_size(src2->type), arange_ne, arange_nb, GGML_MAX_DIMS);
+            ggml_type_size(src2->type), theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
         aclnn_div(ctx, acl_theta_scale_tensor, acl_freq_factors_tensor);
         ACL_CHECK(aclDestroyTensor(acl_freq_factors_tensor));
     }
@@ -2203,20 +2188,19 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
     // position
     GGML_ASSERT(src1->type == GGML_TYPE_I32);
     int64_t position_length = src1->ne[0];
-    int64_t position_ne[] = {1, position_length, 1, 1};
-    size_t position_nb[] = {sizeof(int32_t), sizeof(int32_t),
-                            sizeof(int32_t) * position_length,
+    int64_t position_ne[] = {1, 1, position_length, 1};
+    size_t position_nb[] = {sizeof(int32_t), sizeof(int32_t), sizeof(int32_t),
                             sizeof(int32_t) * position_length};
     aclTensor* acl_position_tensor = ggml_cann_create_tensor(
         src1->data, ggml_cann_type_mapping(src1->type),
         ggml_type_size(src1->type), position_ne, position_nb, GGML_MAX_DIMS);
 
     // power * position
-    int64_t theta_length = arange_length * position_length;
+    int64_t theta_length = theta_scale_length * position_length;
     ggml_cann_pool_alloc theta_allocator(ctx.pool(),
                                          theta_length * sizeof(float_t));
     void* theta_buffer = theta_allocator.get();
-    int64_t theta_ne[] = {arange_length, position_length, 1, 1};
+    int64_t theta_ne[] = {theta_scale_length, 1, position_length, 1};
     size_t theta_nb[GGML_MAX_DIMS];
     theta_nb[0] = sizeof(float_t);
     for (int i = 1; i < GGML_MAX_DIMS; i++) {
@@ -2228,40 +2212,22 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
     aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor,
               acl_theta_tensor);
 
-    // permute: [0,1,2,3]->[0,2,1,3]
-    int64_t permute_ne[] = {arange_length, 1, position_length, 1};
-    size_t permute_nb[GGML_MAX_DIMS];
-    permute_nb[0] = sizeof(float_t);
-    for (int i = 1; i < GGML_MAX_DIMS; i++) {
-        permute_nb[i] = permute_nb[i - 1] * permute_ne[i - 1];
-    }
-    ggml_cann_pool_alloc permute_allocator(ctx.pool(),
-                                           theta_length * sizeof(float_t));
-    void* permute_buffer = permute_allocator.get();
-    aclTensor* acl_permute_tensor = ggml_cann_create_tensor(
-        permute_buffer, ACL_FLOAT, sizeof(float_t), permute_ne, permute_nb,
-        GGML_MAX_DIMS, ACL_FORMAT_ND);
-    int64_t permute_dim[] = {0, 2, 1, 3};
-    int64_t num_dims = 4;
-    aclnn_permute(ctx, acl_theta_tensor, acl_permute_tensor, permute_dim,
-                  num_dims);
-
     // sin/cos
     ggml_cann_pool_alloc sin_allocator(ctx.pool(),
                                        theta_length * sizeof(float_t));
     void* sin_buffer = sin_allocator.get();
     aclTensor* acl_sin_tensor = ggml_cann_create_tensor(
-        sin_buffer, ACL_FLOAT, sizeof(float_t), permute_ne, permute_nb,
+        sin_buffer, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
         GGML_MAX_DIMS, ACL_FORMAT_ND);
-    aclnn_sin(ctx, acl_permute_tensor, acl_sin_tensor);
+    aclnn_sin(ctx, acl_theta_tensor, acl_sin_tensor);
 
     ggml_cann_pool_alloc cos_allocator(ctx.pool(),
                                        theta_length * sizeof(float_t));
     void* cos_buffer = cos_allocator.get();
     aclTensor* acl_cos_tensor = ggml_cann_create_tensor(
-        cos_buffer, ACL_FLOAT, sizeof(float_t), permute_ne, permute_nb,
+        cos_buffer, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
         GGML_MAX_DIMS, ACL_FORMAT_ND);
-    aclnn_cos(ctx, acl_permute_tensor, acl_cos_tensor);
+    aclnn_cos(ctx, acl_theta_tensor, acl_cos_tensor);
 
     // attn_factor
     if (attn_factor != 1) {
@@ -2277,7 +2243,7 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
     } else {
         int64_t num_repeats = 2;
         int64_t dim = 3;
-        int64_t output_size = arange_length * num_repeats;
+        int64_t output_size = theta_scale_length * num_repeats;
         aclnn_repeat_interleave(ctx, acl_sin_tensor, acl_sin_repeat_tensor, dim,
                                 num_repeats, output_size);
         aclnn_repeat_interleave(ctx, acl_cos_tensor, acl_cos_repeat_tensor, dim,
@@ -2285,13 +2251,12 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
     }
 
     // release
-    ACL_CHECK(aclDestroyTensor(acl_arange_tensor));
     ACL_CHECK(aclDestroyTensor(acl_theta_scale_tensor));
     ACL_CHECK(aclDestroyTensor(acl_position_tensor));
     ACL_CHECK(aclDestroyTensor(acl_theta_tensor));
-    ACL_CHECK(aclDestroyTensor(acl_permute_tensor));
     ACL_CHECK(aclDestroyTensor(acl_sin_tensor));
     ACL_CHECK(aclDestroyTensor(acl_cos_tensor));
+    ACL_CHECK(aclDestroyScalar(acl_theta_scale));
 }
 
 #ifdef __cplusplus
@@ -2313,7 +2278,6 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     // TODO: use ascendc
     // Only test with LLAMA model.
     ggml_tensor* src0 = dst->src[0];  // input
-    // ggml_tensor* src2 = dst->src[2];  // freq_factors, not used now.
 
     // param
     float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
@@ -2348,13 +2312,13 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
 
     // init cos/sin cache
     ggml_cann_pool_alloc sin_allocator(
-        ctx.pool(), src0->ne[0] * src0->ne[2] * sizeof(float_t));
+        ctx.pool(), ne00 * ne02 * sizeof(float_t));
     ggml_cann_pool_alloc cos_allocator(
-        ctx.pool(), src0->ne[0] * src0->ne[2] * sizeof(float_t));
+        ctx.pool(), ne00 * ne02 * sizeof(float_t));
     void* sin_buffer = sin_allocator.get();
     void* cos_buffer = cos_allocator.get();
 
-    int64_t sin_reshape_ne[4] = {src0->ne[0], 1, src0->ne[2], 1};
+    int64_t sin_reshape_ne[4] = {ne00, 1, ne02, 1};
     size_t sin_reshape_nb[GGML_MAX_DIMS];
     sin_reshape_nb[0] = sizeof(float_t);
     for (int i = 1; i < GGML_MAX_DIMS; i++) {
@@ -2367,7 +2331,7 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
         ggml_cann_create_tensor(cos_buffer, ACL_FLOAT, sizeof(float_t),
                                 sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
     aclnn_cache_init(ctx, dst, acl_cos_reshape_tensor, acl_sin_reshape_tensor,
-                     theta_scale, freq_scale, attn_factor, is_neox);
+                    theta_scale, freq_scale, attn_factor, is_neox);
 
     aclTensor* acl_src = ggml_cann_create_tensor(src0);
     aclTensor* acl_dst = ggml_cann_create_tensor(dst);
@@ -2544,46 +2508,51 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     return;
 #endif
 
-    // src0 == GGML_TYPE_F16
-    // TODO: optimization this `if` code
-    if (src0->type == GGML_TYPE_F16) {
-        ggml_cann_pool_alloc sin_final_allocator(
-            ctx.pool(), src0->ne[0] * src0->ne[2] * ggml_type_size(src0->type));
-        ggml_cann_pool_alloc cos_final_allocator(
-            ctx.pool(), src0->ne[0] * src0->ne[2] * ggml_type_size(src0->type));
-        void* sin_final_buffer = sin_final_allocator.get();
-        void* cos_final_buffer = cos_final_allocator.get();
+    // ggml_mode = 0 --> aclnn_model = 1
+    int64_t acl_mode = mode == 0 ? 1 : mode;
 
-        int64_t sin_final_ne[4] = {src0->ne[0], 1, src0->ne[2], 1};
-        size_t sin_final_nb[GGML_MAX_DIMS];
-        sin_final_nb[0] = ggml_type_size(src0->type);
-        for (int i = 1; i < GGML_MAX_DIMS; i++) {
-            sin_final_nb[i] = sin_final_nb[i - 1] * sin_final_ne[i - 1];
+    switch (src0->type) {
+        case GGML_TYPE_F32: {
+            GGML_CANN_CALL_ACLNN_OP(RotaryPositionEmbedding, acl_src, acl_cos_reshape_tensor,
+                acl_sin_reshape_tensor, acl_mode, acl_dst);
+            break;
         }
-        aclTensor* acl_sin_final_tensor = ggml_cann_create_tensor(
-            sin_final_buffer, ggml_cann_type_mapping(src0->type),
-            ggml_type_size(src0->type), sin_final_ne, sin_final_nb,
-            GGML_MAX_DIMS);
-        aclTensor* acl_cos_final_tensor = ggml_cann_create_tensor(
-            cos_final_buffer, ggml_cann_type_mapping(src0->type),
-            ggml_type_size(src0->type), sin_final_ne, sin_final_nb,
-            GGML_MAX_DIMS);
+        case GGML_TYPE_F16: {
+            ggml_cann_pool_alloc src_trans_allocator(
+                ctx.pool(), ggml_nelements(src0) * sizeof(float));
+            void* src_trans_buffer = src_trans_allocator.get();
+            ggml_cann_pool_alloc dst_trans_allocator(
+                ctx.pool(), ggml_nelements(dst) * sizeof(float));
+            void* dst_trans_buffer = dst_trans_allocator.get();
 
-        aclnn_cast(ctx, acl_sin_reshape_tensor, acl_sin_final_tensor, dst);
-        aclnn_cast(ctx, acl_cos_reshape_tensor, acl_cos_final_tensor, dst);
-        ACL_CHECK(aclDestroyTensor(acl_cos_reshape_tensor));
-        ACL_CHECK(aclDestroyTensor(acl_sin_reshape_tensor));
-        acl_sin_reshape_tensor = acl_sin_final_tensor;
-        acl_cos_reshape_tensor = acl_cos_final_tensor;
+            size_t src_trans_nb[GGML_MAX_DIMS];
+            src_trans_nb[0] = sizeof(float);
+            for (int i = 1; i < GGML_MAX_DIMS; i++) {
+                src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
+            }
+
+            aclTensor* acl_src_trans_tensor = ggml_cann_create_tensor(
+                src_trans_buffer, ACL_FLOAT, sizeof(float), src0->ne, src_trans_nb,
+                GGML_MAX_DIMS);
+            aclTensor* acl_dst_trans_tensor = ggml_cann_create_tensor(
+                dst_trans_buffer, ACL_FLOAT, sizeof(float), dst->ne, src_trans_nb,
+                GGML_MAX_DIMS);
+
+            aclnn_cast(ctx, acl_src, acl_src_trans_tensor, ACL_FLOAT);
+
+            GGML_CANN_CALL_ACLNN_OP(RotaryPositionEmbedding, acl_src_trans_tensor, acl_cos_reshape_tensor,
+                acl_sin_reshape_tensor, acl_mode, acl_dst_trans_tensor);
+
+            aclnn_cast(ctx, acl_dst_trans_tensor, acl_dst, ACL_FLOAT16);
+
+            ACL_CHECK(aclDestroyTensor(acl_src_trans_tensor));
+            ACL_CHECK(aclDestroyTensor(acl_dst_trans_tensor));
+            break;
+        }
+        default:
+            GGML_ABORT("Unsupported tensor type for GGML_OP_ROPE");
+            break;
     }
-
-    int acl_mode = mode;
-    if (mode == 0) {
-        acl_mode = 1;
-    }
-
-    GGML_CANN_CALL_ACLNN_OP(RotaryPositionEmbedding, acl_src, acl_cos_reshape_tensor,
-                  acl_sin_reshape_tensor, acl_mode, acl_dst);
     ACL_CHECK(aclDestroyTensor(acl_src));
     ACL_CHECK(aclDestroyTensor(acl_cos_reshape_tensor));
     ACL_CHECK(aclDestroyTensor(acl_sin_reshape_tensor));
@@ -2598,6 +2567,7 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     aclTensor* acl_dst = ggml_cann_create_tensor(dst, dst->ne, dst->nb, 3);
 
     GGML_CANN_CALL_ACLNN_OP(ArgMax, acl_src, 3, false, acl_dst);
+
     ACL_CHECK(aclDestroyTensor(acl_src));
     ACL_CHECK(aclDestroyTensor(acl_dst));
 }
@@ -2624,11 +2594,18 @@ void ggml_cann_conv_transpose_1d(ggml_backend_cann_context& ctx, ggml_tensor* ds
     int64_t groups = 1;
     int8_t cubeMathType = 0;
 
+#ifdef ASCEND_310P
+    cubeMathType = 1;
+#endif
+
     GGML_CANN_CALL_ACLNN_OP(Convolution, acl_input, acl_weight, nullptr, stride,
         padding, dilation, transposed, padding, groups, acl_dst, cubeMathType);
 
     ACL_CHECK(aclDestroyTensor(acl_weight));
     ACL_CHECK(aclDestroyTensor(acl_dst));
+    ACL_CHECK(aclDestroyIntArray(stride));
+    ACL_CHECK(aclDestroyIntArray(padding));
+    ACL_CHECK(aclDestroyIntArray(dilation));
 }
 
 void ggml_cann_elu(ggml_backend_cann_context& ctx, ggml_tensor* dst){
@@ -2646,4 +2623,79 @@ void ggml_cann_elu(ggml_backend_cann_context& ctx, ggml_tensor* dst){
 
     ACL_CHECK(aclDestroyTensor(acl_input));
     ACL_CHECK(aclDestroyTensor(acl_dst));
+    ACL_CHECK(aclDestroyScalar(alpha));
+}
+
+void ggml_cann_mean(ggml_backend_cann_context& ctx, ggml_tensor* dst){
+    ggml_tensor * src0 = dst->src[0];
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src0);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    int64_t reduceDimValue[] = {3};
+    aclIntArray* reduceDim = aclCreateIntArray(reduceDimValue, 1);
+    bool keepDim = true;
+
+    GGML_CANN_CALL_ACLNN_OP(Mean, acl_src, reduceDim, keepDim, ACL_FLOAT, acl_dst);
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+    ACL_CHECK(aclDestroyIntArray(reduceDim));
+}
+
+void ggml_cann_pad_reflect_1d(ggml_backend_cann_context& ctx, ggml_tensor* dst){
+    ggml_tensor * src0 = dst->src[0];
+    int32_t *opts = (int32_t *) dst->op_params;
+    int64_t paddingsArray[2] = {opts[0], opts[1]};
+    aclIntArray* paddings = aclCreateIntArray(paddingsArray, 2);
+
+    for (int64_t i = 0; i < src0->ne[3]; i++) {
+        aclTensor* acl_src = ggml_cann_create_tensor(
+            (char*)src0->data + i * src0->ne[3],
+            ggml_cann_type_mapping(src0->type), ggml_element_size(src0),
+            src0->ne, src0->nb, 3);
+
+        aclTensor* acl_dst = ggml_cann_create_tensor(
+            (char*)dst->data + i * src0->ne[3],
+            ggml_cann_type_mapping(dst->type), ggml_element_size(dst),
+            dst->ne, dst->nb, 3);
+
+            GGML_CANN_CALL_ACLNN_OP(ReflectionPad1d, acl_src, paddings, acl_dst);
+
+            ACL_CHECK(aclDestroyTensor(acl_src));
+            ACL_CHECK(aclDestroyTensor(acl_dst));
+    }
+    ACL_CHECK(aclDestroyIntArray(paddings));
+}
+
+void ggml_cann_count_equal(ggml_backend_cann_context& ctx, ggml_tensor* dst){
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src1 = dst->src[1];
+
+    aclTensor* acl_self = ggml_cann_create_tensor(src0);
+    aclTensor* acl_other = ggml_cann_create_tensor(src1);
+
+    GGML_CANN_CALL_ACLNN_OP(InplaceEqTensor, acl_self, acl_other);
+
+    ggml_cann_sum(ctx, dst);
+
+    ACL_CHECK(aclDestroyTensor(acl_self));
+    ACL_CHECK(aclDestroyTensor(acl_other));
+}
+
+void ggml_cann_step(ggml_backend_cann_context& ctx, ggml_tensor* dst){
+    ggml_tensor * src0 = dst->src[0];
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src0);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    float alphaValue = 0.0f;
+    aclScalar* alpha = nullptr;
+    alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT);
+
+    GGML_CANN_CALL_ACLNN_OP(GtScalar, acl_src, alpha, acl_dst);
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+    ACL_CHECK(aclDestroyScalar(alpha));
 }

ggml/src/ggml-cann/aclnn_ops.h

@@ -42,6 +42,8 @@
 #include <aclnnop/aclnn_sqrt.h>
 #include <aclnnop/aclnn_sin.h>
 #include <aclnnop/aclnn_cos.h>
+#include <aclnnop/aclnn_log.h>
+#include <aclnnop/aclnn_sign.h>
 #include "acl_tensor.h"
 #include "common.h"
 
@@ -650,6 +652,67 @@ void ggml_cann_conv_transpose_1d(ggml_backend_cann_context& ctx, ggml_tensor* ds
  */
 void ggml_cann_elu(ggml_backend_cann_context& ctx, ggml_tensor* dst);
 
+/**
+ * @brief   Computes the mean of a ggml tensor element-wise using the CANN backend.
+ *
+ * @details This function calculates the element-wise mean of the input tensor.
+ *          The result is written to the destination tensor `dst`.
+ *          The mean is computed by averaging the values across the entire tensor.
+ *
+ *          This operation is optimized using the CANN backend for high-performance inference or training.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the mean result will be stored.
+ *            dst->op is expected to be `GGML_OP_MEAN`.
+ */
+void ggml_cann_mean(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Applies 1D reflect padding to a ggml tensor using the CANN backend.
+ *
+ * @details This function performs 1D reflect padding on the input tensor.
+ *          The amount of padding on each side is specified by parameters stored in `dst->op_params`.
+ *          The operation reflects the values at the borders of the tensor to generate the padded output.
+ *
+ *          This operation is optimized using the CANN backend for high-performance inference or training.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the padded result will be stored.
+ *            dst->op is expected to be `GGML_OP_PAD_REFLECT_1D`.
+ */
+void ggml_cann_pad_reflect_1d(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Counts the number of equal elements in two ggml tensors using the CANN backend.
+ *
+ * @details This function performs an element-wise comparison between two input tensors,
+ *          and counts the number of positions where the elements are equal. The result is
+ *          stored in the destination tensor `dst` as a scalar.
+ *
+ *          The operation is optimized using the CANN backend, making it suitable for
+ *          high-performance inference or training scenarios.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the result will be stored.
+ *            dst->op is expected to be `GGML_OP_COUNT_EQUAL`.
+ */
+void ggml_cann_count_equal(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Applies the Step activation function to a ggml tensor using the CANN backend.
+ *
+ * @details This function applies a step function element-wise to the input tensor, where
+ *          each element is transformed to 1.0 if it is greater than 0, and 0.0 otherwise.
+ *          The result is stored in the destination tensor `dst`.
+ *
+ *          This operation is accelerated using the CANN backend to improve runtime performance.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the result will be stored.
+ *            dst->op is expected to be `GGML_OP_STEP`.
+ */
+void ggml_cann_step(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
 /**
  * @brief Applies a element-wise operation to two input tensors using the CANN
  * backend.

ggml/src/ggml-cann/ggml-cann.cpp

@@ -29,6 +29,8 @@
 #include <cstdio>
 #include <cstring>
 #include <mutex>
+#include <queue>
+#include <chrono>
 
 #include "ggml-impl.h"
 #include "ggml-backend-impl.h"
@@ -119,9 +121,10 @@ static ggml_cann_device_info ggml_cann_init() {
         prop.location.type = ACL_MEM_LOCATION_TYPE_DEVICE;
         prop.location.id = id;
         prop.reserve = 0;
-        ACL_CHECK(aclrtMemGetAllocationGranularity(
+        err = aclrtMemGetAllocationGranularity(
             &prop, ACL_RT_MEM_ALLOC_GRANULARITY_RECOMMENDED,
-            &info.devices[id].vmm_granularity));
+            &info.devices[id].vmm_granularity);
+        info.devices[id].vmm = err == ACL_SUCCESS;
 
         size_t free, total;
         ggml_backend_cann_get_device_memory(id, &free, &total);
@@ -148,11 +151,223 @@ const ggml_cann_device_info& ggml_cann_info() {
 
 //#define DEBUG_CANN_MALLOC
 /**
- * @brief A pool of CANN buffers(legacy).
+ * @brief A pool of CANN buffers(priority segment buffer).
  *
  * This class manages a pool of CANN buffers for a specific device.
  */
-struct ggml_cann_pool_leg : public ggml_cann_pool {
+struct ggml_cann_pool_buf_prio : public ggml_cann_pool {
+    /**
+     * @brief The maximum reuse margin for a buffer.
+     */
+    static const size_t max_reuse_margin = 1ull << 22;  // 4MB
+
+    /**
+     * @brief The minimum free margin for a buffer.
+     */
+    static const size_t min_free_margin = 1ull << 20;   // 1MB
+
+    /**
+     * @brief The alignment for buffer allocation.
+     */
+    static const size_t alignment = 128;
+
+    /**
+     * @brief The device ID associated with this buffer pool.
+     */
+    int device;
+
+    /**
+     * @brief Whether to disable clean during buffer allocation.
+     */
+    bool disable_clean = false;
+
+    /**
+     * @brief Structure representing a CANN buffer.
+     */
+    struct ggml_cann_buffer {
+        void* ptr = nullptr;  ///< Pointer to the buffer.
+        size_t size = 0;      ///< Size of the buffer.
+        std::chrono::steady_clock::time_point last_used;  ///< Last used time.
+
+        bool operator>(const ggml_cann_buffer& other) const {
+            return size > other.size;
+        }
+    };
+
+    /**
+     * @brief Array of CANN buffers in the pool.
+     */
+    std::unordered_map<void*, size_t> buffer_pool;
+    std::priority_queue<ggml_cann_buffer,
+                        std::vector<ggml_cann_buffer>,
+                        std::greater<>> free_buffers ;
+
+    /**
+     * @brief Total size of all buffers in the pool.
+     */
+    size_t pool_size = 0;
+
+    /**
+     * @brief Constructor to initialize the buffer pool for a specific device.
+     *
+     * @param device The device ID to associate with this buffer pool.
+     */
+    explicit ggml_cann_pool_buf_prio(int device) : device(device) {
+        disable_clean = getenv("GGML_CANN_DISABLE_BUF_POOL_CLEAN") != nullptr;
+    }
+
+    /**
+     * @brief Destructor to free all buffers in the pool.
+     */
+    ~ggml_cann_pool_buf_prio() {
+        ggml_cann_set_device(device);
+        for (auto& [b_ptr, b_size] : buffer_pool) {
+            aclrtFree(b_ptr);
+            pool_size -= b_size;
+        }
+        buffer_pool.clear();
+        GGML_ASSERT(pool_size == 0);
+    }
+
+    /**
+     * @brief Allocate a buffer of the given size.
+     *
+     * @param size The size of the buffer to allocate.
+     * @param actual_size A pointer to a variable to receive the actual size of
+     * the allocated buffer.
+     * @return A pointer to the allocated buffer.
+     */
+    void* alloc(size_t size, size_t* actual_size) override {
+        size = GGML_PAD(size, alignment);
+        if (size == 0) {
+            size = alignment;
+        }
+
+        void* ptr = nullptr;
+        auto now = std::chrono::steady_clock::now();
+
+        std::vector<ggml_cann_buffer> free_buffers_rest;
+        free_buffers_rest.reserve(free_buffers.size());
+        while (!free_buffers.empty()) {
+            auto b = free_buffers.top();
+            free_buffers.pop();
+
+            if (b.size >= size) {
+                // reuse the buffer if the size is enough
+                const size_t margin = b.size - size;
+                if (margin <= max_reuse_margin) {
+                    *actual_size = b.size;
+                    ptr = b.ptr;
+#ifdef DEBUG_CANN_MALLOC
+                    GGML_LOG_INFO(
+                        "cann pool[%d]: reused   %p, "
+                        "pool_size = %5u MB, "
+                        "size = %5u MB, "
+                        "margin = %5u MB\n",
+                        device, b.ptr,
+                        (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576),
+                        (uint32_t)(GGML_PAD(size, 1048576) / 1048576),
+                        (uint32_t)(GGML_PAD(margin, 1048576) / 1048576));
+#endif
+                    break;
+                }
+            }
+
+            bool should_clean = !disable_clean &&
+                                b.size > min_free_margin &&
+                                std::chrono::duration_cast<std::chrono::milliseconds>(now - b.last_used).count() > 100;
+            if (should_clean) {
+                // free the buffer if the size is needed to be freed
+                ACL_CHECK(aclrtFree(b.ptr));
+                pool_size -= b.size;
+                buffer_pool.erase(b.ptr);
+#ifdef DEBUG_CANN_MALLOC
+                GGML_LOG_INFO(
+                    "cann pool[%d]: clean    %p, "
+                    "pool_size = %5u MB, "
+                    "size = %5u MB\n",
+                    device, b.ptr,
+                    (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576),
+                    (uint32_t)(GGML_PAD(b.size, 1048576) / 1048576));
+#endif
+                continue;
+            }
+            free_buffers_rest.push_back(b);
+        }
+        for (ggml_cann_buffer &b : free_buffers_rest) {
+            free_buffers.push(std::move(b));
+        }
+
+#ifdef DEBUG_CANN_MALLOC
+        GGML_LOG_INFO("cann pool[%d] free pool_size = %5u MB\n\n", device, (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576));
+#endif
+        if (ptr != nullptr) {
+            return ptr;
+        }
+
+        // allocate a new buffer if no buffer can be reused
+        ggml_cann_set_device(device);
+        ACL_CHECK(aclrtMalloc(&ptr, size, ACL_MEM_MALLOC_HUGE_FIRST));
+        *actual_size = size;
+        pool_size += size;
+#ifdef DEBUG_CANN_MALLOC
+        GGML_LOG_INFO(
+            "cann pool[%d]: allocate %p, "
+            "pool_size = %5u MB, "
+            "size = %5u MB\n",
+            device, ptr, (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576),
+            (uint32_t)(GGML_PAD(size, 1048576) / 1048576));
+#endif
+        buffer_pool.emplace(ptr, size);
+        return ptr;
+    }
+
+    /**
+     * @brief Free a buffer and return it to the pool.
+     *
+     * @param ptr Pointer to the buffer to free.
+     * @param size Size of the buffer to free.
+     */
+    void free(void* ptr, size_t size) override {
+        GGML_UNUSED(size);
+        auto it = buffer_pool.find(ptr);
+        if (it == buffer_pool.end()) {
+            GGML_ABORT("cann pool[%d]: buffer %p not found in pool\n", device, ptr);
+        }
+
+        auto now = std::chrono::steady_clock::now();
+        free_buffers.emplace(ggml_cann_buffer{ptr, it->second, now});
+#ifdef DEBUG_CANN_MALLOC
+        GGML_LOG_INFO(
+            "cann pool[%d]: return   %p, "
+            "pool_size = %5u MB\n",
+            device, ptr,
+            (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576));
+#endif
+    }
+};
+
+/**
+ * @brief A pool of CANN buffers(segment buffer).
+ *
+ * This class manages a pool of CANN buffers for a specific device.
+ */
+struct ggml_cann_pool_buf : public ggml_cann_pool {
+    /**
+     * @brief The maximum reuse margin for a buffer.
+     */
+    static const size_t max_reuse_margin = 1ull << 22;  // 4MB
+
+    /**
+     * @brief The minimum free margin for a buffer.
+     */
+    static const size_t min_free_margin = 1ull << 20;   // 1MB
+
+    /**
+     * @brief The alignment for buffer allocation.
+     */
+    static const size_t alignment = 128;
+
     /**
      * @brief The maximum number of buffers in the pool.
      */
@@ -163,12 +378,19 @@ struct ggml_cann_pool_leg : public ggml_cann_pool {
      */
     int device;
 
+    /**
+     * @brief Whether to disable clean during buffer allocation.
+     */
+    bool disable_clean = false;
+
     /**
      * @brief Structure representing a CANN buffer.
      */
     struct ggml_cann_buffer {
         void* ptr = nullptr;  ///< Pointer to the buffer memory.
         size_t size = 0;      ///< Size of the buffer.
+        bool used = false;    ///< Whether the buffer is currently in use.
+        std::chrono::steady_clock::time_point last_used;  ///< Last used time.
     };
 
     /**
@@ -186,17 +408,19 @@ struct ggml_cann_pool_leg : public ggml_cann_pool {
      *
      * @param device The device ID to associate with this buffer pool.
      */
-    explicit ggml_cann_pool_leg(int device) : device(device) {}
+    explicit ggml_cann_pool_buf(int device) : device(device) {
+        disable_clean = getenv("GGML_CANN_DISABLE_BUF_POOL_CLEAN") != nullptr;
+    }
 
     /**
      * @brief Destructor to free all buffers in the pool.
      */
-    ~ggml_cann_pool_leg() {
+    ~ggml_cann_pool_buf() {
         ggml_cann_set_device(device);
         for (int i = 0; i < MAX_BUFFERS; ++i) {
             ggml_cann_buffer& b = buffer_pool[i];
             if (b.ptr != nullptr) {
-                ACL_CHECK(aclrtFree(b.ptr));
+                aclrtFree(b.ptr);
                 pool_size -= b.size;
             }
         }
@@ -212,63 +436,93 @@ struct ggml_cann_pool_leg : public ggml_cann_pool {
      * @return A pointer to the allocated buffer.
      */
     void* alloc(size_t size, size_t* actual_size) override {
-        const size_t alignment = 128;
         size = GGML_PAD(size, alignment);
         if (size == 0) {
             size = alignment;
         }
-#ifdef DEBUG_CANN_MALLOC
-        int nnz = 0;
-        size_t max_size = 0;
-#endif
-        size_t best_diff = 1ull << 36;
-        int ibest = -1;
-        for (int i = 0; i < MAX_BUFFERS; ++i) {
+
+        void* ptr = nullptr;
+        auto now = std::chrono::steady_clock::now();
+
+        int i = 0;
+        for (; i < MAX_BUFFERS; ++i) {
             ggml_cann_buffer& b = buffer_pool[i];
-            if (b.ptr != nullptr) {
+            if (b.ptr == nullptr) {
+                break;
+            }
+            if (b.used) {
+                continue;
+            }
+            if (b.size >= size) {
+                // reuse the buffer if the size is enough
+                const size_t margin = b.size - size;
+                if (margin <= max_reuse_margin) {
+                    *actual_size = b.size;
+                    b.used = true;
+                    ptr = b.ptr;
 #ifdef DEBUG_CANN_MALLOC
-                ++nnz;
-                if (b.size > max_size) max_size = b.size;
+                    GGML_LOG_INFO(
+                        "cann pool[%d]: reused   %p, "
+                        "pool_size = %5u MB, "
+                        "size = %5u MB, "
+                        "margin = %5u MB\n",
+                        device, b.ptr,
+                        (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576),
+                        (uint32_t)(GGML_PAD(size, 1048576) / 1048576),
+                        (uint32_t)(GGML_PAD(margin, 1048576) / 1048576));
 #endif
-                if (b.size >= size) {
-                    size_t diff = b.size - size;
-                    if (diff < best_diff) {
-                        best_diff = diff;
-                        ibest = i;
-                        if (!best_diff) {
-                            void* ptr = b.ptr;
-                            *actual_size = b.size;
-                            b.ptr = nullptr;
-                            b.size = 0;
-                            return ptr;
-                        }
-                    }
+                    break;
                 }
             }
+
+            bool should_clean = !disable_clean &&
+                                b.size > min_free_margin &&
+                                std::chrono::duration_cast<std::chrono::milliseconds>(now - b.last_used).count() > 100;
+            if (should_clean) {
+                // free the buffer if the size is needed to be freed
+                ACL_CHECK(aclrtFree(b.ptr));
+                pool_size -= b.size;
+#ifdef DEBUG_CANN_MALLOC
+                GGML_LOG_INFO(
+                    "cann pool[%d]: clean    %p, "
+                    "pool_size = %5u MB, "
+                    "size = %5u MB\n",
+                    device, b.ptr,
+                    (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576),
+                    (uint32_t)(GGML_PAD(b.size, 1048576) / 1048576));
+#endif
+                b.ptr = nullptr;
+            }
         }
-        if (ibest >= 0) {
-            ggml_cann_buffer& b = buffer_pool[ibest];
-            void* ptr = b.ptr;
-            *actual_size = b.size;
-            b.ptr = nullptr;
-            b.size = 0;
+        if (ptr != nullptr) {
             return ptr;
         }
-        void* ptr;
-        ggml_cann_set_device(device);
-        ACL_CHECK(
-            aclrtMalloc(&ptr, size, ACL_MEM_MALLOC_HUGE_FIRST));
-        *actual_size = size;
-        pool_size += size;
+
+        if (i < MAX_BUFFERS) {
+            // allocate a new buffer if no buffer can be reused
+            ggml_cann_buffer& b = buffer_pool[i];
+            ggml_cann_set_device(device);
+            ACL_CHECK(aclrtMalloc(&b.ptr, size, ACL_MEM_MALLOC_HUGE_FIRST));
+            pool_size += size;
+            *actual_size = size;
+            b.size = size;
+            b.used = true;
+            if (i >= MAX_BUFFERS - 8) {
+                GGML_LOG_WARN("cann pool[%d]: slots almost full\n", device);
+            }
 #ifdef DEBUG_CANN_MALLOC
-        GGML_LOG_INFO(
-            "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, "
-            "requested %u MB\n",
-            __func__, device, nnz, (uint32_t)(max_size / 1024 / 1024),
-            (uint32_t)(pool_size / 1024 / 1024),
-            (uint32_t)(size / 1024 / 1024));
+            GGML_LOG_INFO(
+                "cann pool[%d]: allocate %p, "
+                "pool_size = %5u MB, "
+                "size = %5u MB\n",
+                device, b.ptr,
+                (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576),
+                (uint32_t)(GGML_PAD(b.size, 1048576) / 1048576));
 #endif
-        return ptr;
+            return b.ptr;
+        }
+
+        GGML_ABORT("cann pool[%d]: slots full\n", device);
     }
 
     /**
@@ -278,18 +532,24 @@ struct ggml_cann_pool_leg : public ggml_cann_pool {
      * @param size Size of the buffer to free.
      */
     void free(void* ptr, size_t size) override {
+        GGML_UNUSED(size);
         for (int i = 0; i < MAX_BUFFERS; ++i) {
             ggml_cann_buffer& b = buffer_pool[i];
-            if (b.ptr == nullptr) {
-                b.ptr = ptr;
-                b.size = size;
-                return;
+            if (b.ptr != ptr) {
+                continue;
             }
+            b.used = false;
+            b.last_used = std::chrono::steady_clock::now();
+#ifdef DEBUG_CANN_MALLOC
+            GGML_LOG_INFO(
+                "cann pool[%d]: return   %p, "
+                "pool_size = %5u MB\n",
+                device, b.ptr,
+                (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576));
+#endif
+            return;
         }
-        // memory should always buffered. these memory may still needed by
-        // tasks in stream.
-        // TODO, fix me.
-        GGML_ABORT("Cann buffer pool full, increase MAX_CANN_BUFFERS\n");
+        GGML_ABORT("cann pool[%d]: slots full\n", device);
     }
 };
 
@@ -347,8 +607,7 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool {
      * @param device The device ID to associate with this buffer pool.
      */
     explicit ggml_cann_pool_vmm(int device)
-        : device(device),
-          granularity(ggml_cann_info().devices[device].vmm_granularity) {
+    : device(device) {
         auto dev = ggml_cann_info().devices[device];
         granularity = dev.vmm_granularity;
         max_size = dev.total_vram;
@@ -471,7 +730,18 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool {
  */
 std::unique_ptr<ggml_cann_pool> ggml_backend_cann_context::new_pool_for_device(
     int device) {
-    return std::unique_ptr<ggml_cann_pool>(new ggml_cann_pool_vmm(device));
+    bool disable_vmm = (getenv("GGML_CANN_DISABLE_VMM_POOL") != nullptr);
+    if (!disable_vmm && ggml_cann_info().devices[device].vmm) {
+        GGML_LOG_INFO("%s: device %d use vmm pool\n", __func__, device);
+        return std::unique_ptr<ggml_cann_pool>(new ggml_cann_pool_vmm(device));
+    }
+    bool enable_buf_prio = (getenv("GGML_CANN_ENABLE_BUF_PRIO_POOL") != nullptr);
+    if (enable_buf_prio) {
+        GGML_LOG_INFO("%s: device %d use buffer pool with priority queue\n", __func__, device);
+        return std::unique_ptr<ggml_cann_pool>(new ggml_cann_pool_buf_prio(device));
+    }
+    GGML_LOG_INFO("%s: device %d use buffer pool\n", __func__, device);
+    return std::unique_ptr<ggml_cann_pool>(new ggml_cann_pool_buf(device));
 }
 
 // cann buffer
@@ -1020,8 +1290,11 @@ ggml_backend_cann_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft,
 
     ggml_cann_set_device(buft_ctx->device);
 
-    size = std::max(size, (size_t)1);
-
+    const size_t alignment = 128;
+    size = GGML_PAD(size, alignment);
+    if (size == 0) {
+        size = alignment;
+    }
     void* dev_ptr;
     aclError err = aclrtMalloc(&dev_ptr, size, ACL_MEM_MALLOC_HUGE_FIRST);
     if (err != ACL_SUCCESS) {
@@ -1358,6 +1631,12 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
                 case GGML_UNARY_OP_ELU:
                     ggml_cann_elu(ctx, dst);
                     break;
+                case GGML_UNARY_OP_SGN:
+                    GGML_CANN_CALL_UNARY_OP(Sign);
+                    break;
+                case GGML_UNARY_OP_STEP:
+                    ggml_cann_step(ctx, dst);
+                    break;
                 default:
                     return false;
             }
@@ -1456,6 +1735,18 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
         case GGML_OP_CONV_TRANSPOSE_1D:
             ggml_cann_conv_transpose_1d(ctx, dst);
             break;
+        case GGML_OP_LOG:
+            GGML_CANN_CALL_UNARY_OP(Log);
+            break;
+        case GGML_OP_MEAN:
+            ggml_cann_mean(ctx, dst);
+            break;
+        case GGML_OP_PAD_REFLECT_1D:
+            ggml_cann_pad_reflect_1d(ctx, dst);
+            break;
+        case GGML_OP_COUNT_EQUAL:
+            ggml_cann_count_equal(ctx, dst);
+            break;
         default:
             return false;
     }
@@ -1718,6 +2009,8 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                 case GGML_UNARY_OP_TANH:
                 case GGML_UNARY_OP_EXP:
                 case GGML_UNARY_OP_ELU:
+                case GGML_UNARY_OP_SGN:
+                case GGML_UNARY_OP_STEP:
                     return true;
                 default:
                     return false;
@@ -1729,6 +2022,10 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                     return true;
                 case GGML_TYPE_Q8_0:
                 case GGML_TYPE_Q4_0:
+#ifdef ASCEND_310P
+                    // Q4 && Q8 per group is not suppor on 310p device
+                    return false;
+#endif
                     // only support contiguous for quantized types.
                     return ggml_is_contiguous(op->src[0]) &&
                             ggml_is_contiguous(op->src[1]);
@@ -1796,6 +2093,9 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                 return false;
             }
 
+            if(!ggml_is_contiguous(op->src[0])){
+                return false;
+            }
             return true;
         }
         case GGML_OP_UPSCALE: {
@@ -1804,10 +2104,19 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
             if (op->src[0]->ne[2] * op->ne[3] != op->src[0]->ne[3] * op->ne[2]) {
                 return false;
             }
+            if (op->op_params[0] != GGML_SCALE_MODE_NEAREST) {
+                return false;
+            }
             return true;
         }
         case GGML_OP_POOL_2D: {
             const int32_t * opts = (const int32_t *) op->op_params;
+#ifdef ASCEND_310P
+            enum ggml_op_pool opt = static_cast<ggml_op_pool>(opts[0]);
+            if(opt == GGML_OP_POOL_MAX){
+                return false;
+            }
+#endif
             const int       k0   = opts[1];
             const int       k1   = opts[2];
             const int       p0   = opts[5];
@@ -1851,6 +2160,10 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
         case GGML_OP_COS:
         case GGML_OP_SIN:
         case GGML_OP_CONV_TRANSPOSE_1D:
+        case GGML_OP_LOG:
+        case GGML_OP_MEAN:
+        case GGML_OP_PAD_REFLECT_1D:
+        case GGML_OP_COUNT_EQUAL:
             return true;
         default:
             return false;

ggml/src/ggml-cpu/ggml-cpu.c

@@ -2027,41 +2027,6 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_rwkv_wkv7(params, tensor);
             } break;
-        case GGML_OP_MAP_UNARY:
-            {
-                ggml_unary_op_f32_t fun;
-                memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_unary(params, tensor, fun);
-            }
-            break;
-        case GGML_OP_MAP_BINARY:
-            {
-                ggml_binary_op_f32_t fun;
-                memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_binary(params, tensor, fun);
-            }
-            break;
-        case GGML_OP_MAP_CUSTOM1_F32:
-            {
-                ggml_custom1_op_f32_t fun;
-                memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_custom1_f32(params, tensor, fun);
-            }
-            break;
-        case GGML_OP_MAP_CUSTOM2_F32:
-            {
-                ggml_custom2_op_f32_t fun;
-                memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_custom2_f32(params, tensor, fun);
-            }
-            break;
-        case GGML_OP_MAP_CUSTOM3_F32:
-            {
-                ggml_custom3_op_f32_t fun;
-                memcpy(&fun, tensor->op_params, sizeof(fun));
-                ggml_compute_forward_map_custom3_f32(params, tensor, fun);
-            }
-            break;
         case GGML_OP_MAP_CUSTOM1:
             {
                 ggml_compute_forward_map_custom1(params, tensor);
@@ -2077,6 +2042,11 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
                 ggml_compute_forward_map_custom3(params, tensor);
             }
             break;
+        case GGML_OP_CUSTOM:
+            {
+                ggml_compute_forward_custom(params, tensor);
+            }
+            break;
         case GGML_OP_CROSS_ENTROPY_LOSS:
             {
                 ggml_compute_forward_cross_entropy_loss(params, tensor);
@@ -2328,11 +2298,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
         case GGML_OP_WIN_PART:
         case GGML_OP_WIN_UNPART:
         case GGML_OP_GET_REL_POS:
-        case GGML_OP_MAP_UNARY:
-        case GGML_OP_MAP_BINARY:
-        case GGML_OP_MAP_CUSTOM1_F32:
-        case GGML_OP_MAP_CUSTOM2_F32:
-        case GGML_OP_MAP_CUSTOM3_F32:
             {
                 n_tasks = 1;
             } break;
@@ -2366,6 +2331,16 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
                     n_tasks = MIN(p.n_tasks, n_threads);
                 }
             } break;
+        case GGML_OP_CUSTOM:
+            {
+                struct ggml_custom_op_params p;
+                memcpy(&p, node->op_params, sizeof(p));
+                if (p.n_tasks == GGML_N_TASKS_MAX) {
+                    n_tasks = n_threads;
+                } else {
+                    n_tasks = MIN(p.n_tasks, n_threads);
+                }
+            } break;
         case GGML_OP_CROSS_ENTROPY_LOSS:
         case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
         case GGML_OP_OPT_STEP_ADAMW:

ggml/src/ggml-cpu/ggml-cpu.cpp

@@ -425,6 +425,8 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
         }
         case GGML_OP_IM2COL_BACK:
             return src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32;
+        case GGML_OP_GET_ROWS_BACK:
+            return src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16;
         case GGML_OP_OUT_PROD:
             return (src0->type == GGML_TYPE_F32 || (ggml_is_quantized(src0->type) && src0->ne[2] == src1->ne[2] && src0->ne[3] == src1->ne[3])) &&
                 src1->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;

ggml/src/ggml-cpu/ops.cpp

@@ -6351,24 +6351,72 @@ static void ggml_compute_forward_upscale_f32(
     const float sf2 = (float)ne2/src0->ne[2];
     const float sf3 = (float)ne3/src0->ne[3];
 
-    // TODO: optimize
+    const ggml_scale_mode mode = (ggml_scale_mode) ggml_get_op_params_i32(dst, 0);
 
-    for (int64_t i3 = 0; i3 < ne3; i3++) {
-        const int64_t i03 = i3 / sf3;
-        for (int64_t i2 = ith; i2 < ne2; i2 += nth) {
-            const int64_t i02 = i2 / sf2;
-            for (int64_t i1 = 0; i1 < ne1; i1++) {
-                const int64_t i01 = i1 / sf1;
-                for (int64_t i0 = 0; i0 < ne0; i0++) {
-                    const int64_t i00 = i0 / sf0;
+    if (mode == GGML_SCALE_MODE_NEAREST) {
+        for (int64_t i3 = 0; i3 < ne3; i3++) {
+            const int64_t i03 = i3 / sf3;
+            for (int64_t i2 = ith; i2 < ne2; i2 += nth) {
+                const int64_t i02 = i2 / sf2;
+                for (int64_t i1 = 0; i1 < ne1; i1++) {
+                    const int64_t i01 = i1 / sf1;
+                    for (int64_t i0 = 0; i0 < ne0; i0++) {
+                        const int64_t i00 = i0 / sf0;
 
-                    const float * x = (float *)((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
-                          float * y = (float *)((char *)  dst->data +  i0*nb0  +  i1*nb1  +  i2*nb2  +  i3*nb3);
+                        const float * x = (float *)((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+                              float * y = (float *)((char *)  dst->data +  i0*nb0  +  i1*nb1  +  i2*nb2  +  i3*nb3);
 
-                    *y = *x;
+                        *y = *x;
+                    }
                 }
             }
         }
+    } else if (mode == GGML_SCALE_MODE_BILINEAR) {
+        // setting a pixel offset of 0 would replicate the behavior of pytorch interpolate with align_corners=True
+        const float pixel_offset = 0.5f;
+
+        for (int64_t i3 = 0; i3 < ne3; i3++) {
+            const int64_t i03 = i3 / sf3;
+            for (int64_t i2 = ith; i2 < ne2; i2 += nth) {
+                const int64_t i02 = i2 / sf2;
+                for (int64_t i1 = 0; i1 < ne1; i1++) {
+                    const float y = ((float)i1 + pixel_offset) / sf1 - pixel_offset;
+                    int64_t y0 = (int64_t)floorf(y);
+                    int64_t y1 = y0 + 1;
+
+                    y0 = std::max(int64_t(0), std::min(y0, ne01 - 1));
+                    y1 = std::max(int64_t(0), std::min(y1, ne01 - 1));
+
+                    float dy = y - (float)y0;
+                    dy = std::max(0.0f, std::min(dy, 1.0f));
+
+                    for (int64_t i0 = 0; i0 < ne0; i0++) {
+                        const float x = ((float)i0 + pixel_offset) / sf0 - pixel_offset;
+                        int64_t x0 = (int64_t)floorf(x);
+                        int64_t x1 = x0 + 1;
+
+                        x0 = std::max(int64_t(0), std::min(x0, ne00 - 1));
+                        x1 = std::max(int64_t(0), std::min(x1, ne00 - 1));
+
+                        float dx = x - (float)x0;
+                        dx = std::max(0.0f, std::min(dx, 1.0f));
+
+                        // fetch the four surrounding pixel values and interpolate
+                        const float a = *(const float *)((const char *)src0->data + x0*nb00 + y0*nb01 + i02*nb02 + i03*nb03);
+                        const float b = *(const float *)((const char *)src0->data + x1*nb00 + y0*nb01 + i02*nb02 + i03*nb03);
+                        const float c = *(const float *)((const char *)src0->data + x0*nb00 + y1*nb01 + i02*nb02 + i03*nb03);
+                        const float d = *(const float *)((const char *)src0->data + x1*nb00 + y1*nb01 + i02*nb02 + i03*nb03);
+
+                        const float val = a*(1 - dx)*(1 - dy) + b*dx*(1 - dy) + c*(1 - dx)*dy + d*dx*dy;
+
+                        float * y_dst = (float *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3);
+                        *y_dst = val;
+                    }
+                }
+            }
+        }
+    } else {
+        GGML_ABORT("unsupported upscale mode");
     }
 }
 
@@ -8268,152 +8316,6 @@ void ggml_compute_forward_rwkv_wkv7(
     }
 }
 
-// ggml_compute_forward_map_unary
-
-static void ggml_compute_forward_map_unary_f32(
-        const ggml_compute_params * params,
-        ggml_tensor * dst,
-        const ggml_unary_op_f32_t fun) {
-
-    const ggml_tensor * src0 = dst->src[0];
-
-    if (params->ith != 0) {
-        return;
-    }
-
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(dst));
-    assert(ggml_are_same_shape(src0, dst));
-
-    const int n  = ggml_nrows(src0);
-    const int nc = src0->ne[0];
-
-    for (int i = 0; i < n; i++) {
-        fun(nc,
-                (float *) ((char *) dst->data  + i*( dst->nb[1])),
-                (float *) ((char *) src0->data + i*(src0->nb[1])));
-    }
-}
-
-void ggml_compute_forward_map_unary(
-        const ggml_compute_params * params,
-        ggml_tensor * dst,
-        const ggml_unary_op_f32_t fun) {
-
-    const ggml_tensor * src0 = dst->src[0];
-
-    switch (src0->type) {
-        case GGML_TYPE_F32:
-            {
-                ggml_compute_forward_map_unary_f32(params, dst, fun);
-            } break;
-        default:
-            {
-                GGML_ABORT("fatal error");
-            }
-    }
-}
-
-// ggml_compute_forward_map_binary
-
-static void ggml_compute_forward_map_binary_f32(
-        const ggml_compute_params * params,
-        ggml_tensor * dst,
-        const ggml_binary_op_f32_t fun) {
-
-    const ggml_tensor * src0 = dst->src[0];
-    const ggml_tensor * src1 = dst->src[1];
-
-    if (params->ith != 0) {
-        return;
-    }
-
-    assert(ggml_is_contiguous_1(src0));
-    assert(ggml_is_contiguous_1(src1));
-    assert(ggml_is_contiguous_1(dst));
-    assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
-
-    const int n  = ggml_nrows(src0);
-    const int nc = src0->ne[0];
-
-    for (int i = 0; i < n; i++) {
-        fun(nc,
-                (float *) ((char *) dst->data  + i*( dst->nb[1])),
-                (float *) ((char *) src0->data + i*(src0->nb[1])),
-                (float *) ((char *) src1->data + i*(src1->nb[1])));
-    }
-}
-
-void ggml_compute_forward_map_binary(
-        const ggml_compute_params * params,
-        ggml_tensor * dst,
-        const ggml_binary_op_f32_t fun) {
-
-    const ggml_tensor * src0 = dst->src[0];
-
-    switch (src0->type) {
-        case GGML_TYPE_F32:
-            {
-                ggml_compute_forward_map_binary_f32(params, dst, fun);
-            } break;
-        default:
-            {
-                GGML_ABORT("fatal error");
-            }
-    }
-}
-
-// ggml_compute_forward_map_custom1
-
-void ggml_compute_forward_map_custom1_f32(
-        const ggml_compute_params * params,
-        ggml_tensor * dst,
-        const ggml_custom1_op_f32_t fun) {
-
-    const ggml_tensor * a = dst->src[0];
-
-    if (params->ith != 0) {
-        return;
-    }
-
-    fun(dst, a);
-}
-
-// ggml_compute_forward_map_custom2
-
-void ggml_compute_forward_map_custom2_f32(
-        const ggml_compute_params * params,
-        ggml_tensor * dst,
-        const ggml_custom2_op_f32_t fun) {
-
-    const ggml_tensor * a = dst->src[0];
-    const ggml_tensor * b = dst->src[1];
-
-    if (params->ith != 0) {
-        return;
-    }
-
-    fun(dst, a, b);
-}
-
-// ggml_compute_forward_map_custom3
-
-void ggml_compute_forward_map_custom3_f32(
-        const ggml_compute_params * params,
-        ggml_tensor * dst,
-        const ggml_custom3_op_f32_t fun) {
-
-    const ggml_tensor * a = dst->src[0];
-    const ggml_tensor * b = dst->src[1];
-    const ggml_tensor * c = dst->src[1];
-
-    if (params->ith != 0) {
-        return;
-    }
-
-    fun(dst, a, b, c);
-}
-
 // ggml_compute_forward_map_custom1
 
 void ggml_compute_forward_map_custom1(
@@ -8459,6 +8361,18 @@ void ggml_compute_forward_map_custom3(
     p.fun(dst, a, b, c, params->ith, params->nth, p.userdata);
 }
 
+// ggml_compute_forward_custom
+
+void ggml_compute_forward_custom(
+    const struct ggml_compute_params * params,
+          struct ggml_tensor * dst) {
+
+    struct ggml_custom_op_params p;
+    memcpy(&p, dst->op_params, sizeof(p));
+
+    p.fun(dst, params->ith, params->nth, p.userdata);
+}
+
 // ggml_compute_forward_cross_entropy_loss
 
 static void ggml_compute_forward_cross_entropy_loss_f32(

ggml/src/ggml-cpu/ops.h

@@ -96,29 +96,10 @@ void ggml_compute_forward_add_rel_pos(const struct ggml_compute_params * params,
 void ggml_compute_forward_rwkv_wkv6(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_rwkv_wkv7(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_gla(const struct ggml_compute_params * params, struct ggml_tensor * dst);
-void ggml_compute_forward_map_unary(
-    const struct ggml_compute_params * params,
-    struct ggml_tensor * dst,
-    const ggml_unary_op_f32_t fun);
-void ggml_compute_forward_map_binary(
-    const struct ggml_compute_params * params,
-    struct ggml_tensor * dst,
-    const ggml_binary_op_f32_t fun);
-void ggml_compute_forward_map_custom1_f32(
-    const struct ggml_compute_params * params,
-    struct ggml_tensor * dst,
-    const ggml_custom1_op_f32_t fun);
-void ggml_compute_forward_map_custom2_f32(
-    const struct ggml_compute_params * params,
-    struct ggml_tensor * dst,
-    const ggml_custom2_op_f32_t fun);
-void ggml_compute_forward_map_custom3_f32(
-    const struct ggml_compute_params * params,
-    struct ggml_tensor * dst,
-    const ggml_custom3_op_f32_t fun);
 void ggml_compute_forward_map_custom1(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_map_custom2(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_map_custom3(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_custom(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_cross_entropy_loss(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_cross_entropy_loss_back(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_opt_step_adamw(const struct ggml_compute_params * params, struct ggml_tensor * dst);

ggml/src/ggml-cpu/simd-mappings.h

@@ -855,13 +855,17 @@ static inline __vector float __lzs_f16cx4_load(const ggml_fp16_t * x) {
         tmp[i] = GGML_FP16_TO_FP32(x[i]);
     }
 
-    return vec_xl(0, tmp);
+    // note: keep type-cast here to prevent compiler bugs
+    // see: https://github.com/ggml-org/llama.cpp/issues/12846
+    return vec_xl(0, (const float *)(tmp));
 }
 
 static inline void __lzs_f16cx4_store(ggml_fp16_t * x, __vector float y) {
     float arr[4];
 
-    vec_xst(y, 0, arr);
+    // note: keep type-cast here to prevent compiler bugs
+    // see: https://github.com/ggml-org/llama.cpp/issues/12846
+    vec_xst(y, 0, (float *)(arr));
 
     for (int i = 0; i < 4; i++) {
         x[i] = GGML_FP32_TO_FP16(arr[i]);

ggml/src/ggml-cuda/ggml-cuda.cu

@@ -96,31 +96,32 @@ int ggml_cuda_get_device() {
 
 static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) {
     ggml_cuda_set_device(device);
-#if defined(GGML_USE_HIP) && defined(GGML_HIP_UMA)
-    auto res = hipMallocManaged(ptr, size);
-    if (res == hipSuccess) {
-        // if error we "need" to know why...
-        CUDA_CHECK(hipMemAdvise(*ptr, size, hipMemAdviseSetCoarseGrain, device));
-    }
-    return res;
-#else
-
-#if !defined(GGML_USE_HIP)
     cudaError_t err;
     if (getenv("GGML_CUDA_ENABLE_UNIFIED_MEMORY") != nullptr)
     {
         err = cudaMallocManaged(ptr, size);
+#if defined(GGML_USE_HIP)
+        if (err == hipSuccess) {
+            CUDA_CHECK(cudaMemAdvise(*ptr, size, hipMemAdviseSetCoarseGrain, device));
+        }
+
+        // fall back to cudaMalloc if not supported (e.g. on Windows)
+        if (err == hipErrorNotSupported) {
+            static bool warned_unsupported = false;
+            if (!warned_unsupported) {
+                GGML_LOG_WARN("hipMallocManaged unsupported, falling back to hipMalloc.\n");
+                warned_unsupported = true;
+            }
+
+            err = cudaMalloc(ptr, size);
+        }
+#endif // defined(GGML_USE_HIP)
     }
     else
     {
         err = cudaMalloc(ptr, size);
     }
     return err;
-#else
-    return cudaMalloc(ptr, size);
-#endif // !defined(GGML_USE_HIP)
-
-#endif
 }
 
 #if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
@@ -2488,10 +2489,10 @@ static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cud
 #endif
         }
 
-        if (node->op == GGML_OP_MUL_MAT_ID) {
+        if (node->op == GGML_OP_MUL_MAT_ID || node->op == GGML_OP_CONT || node->op == GGML_OP_DUP) {
             use_cuda_graph = false; // This node type is not supported by CUDA graph capture
 #ifndef NDEBUG
-            GGML_LOG_DEBUG("%s: disabling CUDA graphs due to mul_mat_id\n", __func__);
+            GGML_LOG_DEBUG("%s: disabling CUDA graphs due to unsupported node type\n", __func__);
 #endif
         }
 
@@ -3216,6 +3217,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_GROUP_NORM:
             return ggml_is_contiguous(op->src[0]);
         case GGML_OP_UPSCALE:
+            return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST;
         case GGML_OP_PAD:
         case GGML_OP_ARANGE:
         case GGML_OP_TIMESTEP_EMBEDDING:

ggml/src/ggml-cuda/vendors/hip.h

@@ -71,6 +71,8 @@
 #define cudaLaunchHostFunc hipLaunchHostFunc
 #define cudaMalloc hipMalloc
 #define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
+#define cudaMallocManaged hipMallocManaged
+#define cudaMemAdvise hipMemAdvise
 #define cudaMemcpy hipMemcpy
 #define cudaMemcpyAsync hipMemcpyAsync
 #define cudaMemcpyPeerAsync hipMemcpyPeerAsync

ggml/src/ggml-hip/CMakeLists.txt

@@ -89,10 +89,6 @@ endif()
 
 add_compile_definitions(GGML_USE_HIP)
 
-if (GGML_HIP_UMA)
-    add_compile_definitions(GGML_HIP_UMA)
-endif()
-
 if (GGML_CUDA_FORCE_MMQ)
     add_compile_definitions(GGML_CUDA_FORCE_MMQ)
 endif()

ggml/src/ggml-impl.h

@@ -16,6 +16,14 @@
 #include <arm_sve.h>
 #endif // __ARM_FEATURE_SVE
 
+#if defined(__ARM_NEON) && !defined(__CUDACC__) && !defined(__MUSACC__)
+// if YCM cannot find <arm_neon.h>, make a symbolic link to it, for example:
+//
+//   $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/
+//
+#include <arm_neon.h>
+#endif
+
 #if defined(__F16C__)
 #include <immintrin.h>
 #endif
@@ -140,8 +148,14 @@ struct ggml_map_custom2_op_params {
 
 struct ggml_map_custom3_op_params {
     ggml_custom3_op_t fun;
-    int n_tasks;
-    void * userdata;
+    int               n_tasks;
+    void            * userdata;
+};
+
+struct ggml_custom_op_params {
+    ggml_custom_op_t fun;
+    int              n_tasks;
+    void           * userdata;
 };
 
 // bitset
@@ -311,13 +325,6 @@ GGML_API void ggml_aligned_free(void * ptr, size_t size);
 // for     MUSA compilers        , we use uint16_t: ref https://github.com/ggml-org/llama.cpp/pull/11843
 //
 #if defined(__ARM_NEON) && !(defined(__CUDACC__) && __CUDACC_VER_MAJOR__ <= 11) && !defined(__MUSACC__)
-
-    // if YCM cannot find <arm_neon.h>, make a symbolic link to it, for example:
-    //
-    //   $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/
-    //
-    #include <arm_neon.h>
-
     #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
     #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
 

ggml/src/ggml-metal/ggml-metal.m

@@ -402,6 +402,13 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H192,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_HK192_HV128,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H96,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H96,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H96,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H96,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H96,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H96,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H96,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H128,
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H128,
@@ -1059,6 +1066,13 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H192,        flash_attn_ext_q8_0_h192,        has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_HK192_HV128, flash_attn_ext_q8_0_hk192_hv128, has_simdgroup_mm);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_Q8_0_H256,        flash_attn_ext_q8_0_h256,        has_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H96,      flash_attn_ext_vec_f16_h96,      has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H96,     flash_attn_ext_vec_bf16_h96,     has_simdgroup_reduction && use_bfloat);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H96,     flash_attn_ext_vec_q4_0_h96,     has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H96,     flash_attn_ext_vec_q4_1_h96,     has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H96,     flash_attn_ext_vec_q5_0_h96,     has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H96,     flash_attn_ext_vec_q5_1_h96,     has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H96,     flash_attn_ext_vec_q8_0_h96,     has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128,     flash_attn_ext_vec_f16_h128,     has_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H128,    flash_attn_ext_vec_bf16_h128,    has_simdgroup_reduction && use_bfloat);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H128,    flash_attn_ext_vec_q4_0_h128,    has_simdgroup_reduction);
@@ -1334,8 +1348,9 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
             return op->src[0]->type == GGML_TYPE_F16;
         case GGML_OP_POOL_1D:
             return false;
-        case GGML_OP_POOL_2D:
         case GGML_OP_UPSCALE:
+            return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST;
+        case GGML_OP_POOL_2D:
         case GGML_OP_PAD:
         case GGML_OP_PAD_REFLECT_1D:
         case GGML_OP_TIMESTEP_EMBEDDING:
@@ -3842,7 +3857,7 @@ static void ggml_metal_encode_node(
                 // TODO: add vec kernels for (ne00%64 == 0) and maybe also for (ne00%32 == 0)
                 //       for now avoiding mainly to keep the number of templates/kernels a bit lower
                 //       these are now trivial to add after: https://github.com/ggml-org/llama.cpp/pull/12612
-                if (ne01 >= 4 || (ne00%128 != 0 && ne00 != 192)) {
+                if (ne01 >= 4 || (ne00%128 != 0 && ne00 != 96 && ne00 != 192)) {
                     switch (src1->type) {
                         case GGML_TYPE_F16:
                             {
@@ -4009,6 +4024,24 @@ static void ggml_metal_encode_node(
                     use_vec_kernel = true;
 
                     switch (ne00) {
+                        case 96:
+                            {
+                                switch (src1->type) {
+                                    case GGML_TYPE_F16:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H96].pipeline; break;
+                                    case GGML_TYPE_BF16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_BF16_H96].pipeline; break;
+                                    case GGML_TYPE_Q4_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_0_H96].pipeline; break;
+                                    case GGML_TYPE_Q4_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q4_1_H96].pipeline; break;
+                                    case GGML_TYPE_Q5_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_0_H96].pipeline; break;
+                                    case GGML_TYPE_Q5_1: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q5_1_H96].pipeline; break;
+                                    case GGML_TYPE_Q8_0: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_Q8_0_H96].pipeline; break;
+                                    default:
+                                        {
+                                            GGML_LOG_ERROR("unsupported type: %d\n", src1->type);
+                                            GGML_LOG_ERROR("add template specialization for this type\n");
+                                            GGML_ABORT("add template specialization for this type");
+                                        }
+                                }
+                            } break;
                         case 128:
                             {
                                 switch (src1->type) {

ggml/src/ggml-metal/ggml-metal.metal

@@ -3959,6 +3959,16 @@ kernel void kernel_flash_attn_ext_vec(
 
 typedef decltype(kernel_flash_attn_ext_vec<FA_TYPES, half4, 1, dequantize_f16_t4, half4, 1, dequantize_f16_t4, 128, 128, 4>) flash_attn_ext_vec_t;
 
+template [[host_name("kernel_flash_attn_ext_vec_f16_h96")]]  kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, half4,             1, dequantize_f16_t4,  half4,       1, dequantize_f16_t4,  96, 96, 4>;
+#if defined(GGML_METAL_USE_BF16)
+template [[host_name("kernel_flash_attn_ext_vec_bf16_h96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, bfloat4,           1, dequantize_bf16_t4, bfloat4,     1, dequantize_bf16_t4, 96, 96, 4>;
+#endif
+template [[host_name("kernel_flash_attn_ext_vec_q4_0_h96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, block_q4_0,        8, dequantize_q4_0_t4, block_q4_0,  8, dequantize_q4_0_t4, 96, 96, 4>;
+template [[host_name("kernel_flash_attn_ext_vec_q4_1_h96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, block_q4_1,        8, dequantize_q4_1_t4, block_q4_1,  8, dequantize_q4_1_t4, 96, 96, 4>;
+template [[host_name("kernel_flash_attn_ext_vec_q5_0_h96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, block_q5_0,        8, dequantize_q5_0_t4, block_q5_0,  8, dequantize_q5_0_t4, 96, 96, 4>;
+template [[host_name("kernel_flash_attn_ext_vec_q5_1_h96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, block_q5_1,        8, dequantize_q5_1_t4, block_q5_1,  8, dequantize_q5_1_t4, 96, 96, 4>;
+template [[host_name("kernel_flash_attn_ext_vec_q8_0_h96")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, block_q8_0,        8, dequantize_q8_0_t4, block_q8_0,  8, dequantize_q8_0_t4, 96, 96, 4>;
+
 template [[host_name("kernel_flash_attn_ext_vec_f16_h128")]]  kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, half4,             1, dequantize_f16_t4,  half4,       1, dequantize_f16_t4,  128, 128, 4>;
 #if defined(GGML_METAL_USE_BF16)
 template [[host_name("kernel_flash_attn_ext_vec_bf16_h128")]] kernel flash_attn_ext_vec_t kernel_flash_attn_ext_vec<FA_TYPES, bfloat4,           1, dequantize_bf16_t4, bfloat4,     1, dequantize_bf16_t4, 128, 128, 4>;

ggml/src/ggml-opencl/CMakeLists.txt

@@ -54,16 +54,41 @@ function(ggml_opencl_add_kernel KNAME)
 endfunction()
 
 set(GGML_OPENCL_KERNELS
-    ggml-opencl
-    ggml-opencl_mm
-    ggml-opencl_cvt
-    ggml-opencl_gemv_noshuffle
-    ggml-opencl_gemv_noshuffle_general
-    ggml-opencl_mul_mat_Ab_Bi_8x4
-    ggml-opencl_transpose_16
-    ggml-opencl_transpose_32
-    ggml-opencl_transpose_32_16
-    ggml-opencl_im2col
+    add
+    clamp
+    cpy
+    cvt
+    diag_mask_inf
+    gelu
+    gemv_noshuffle_general
+    gemv_noshuffle
+    get_rows
+    im2col_f32
+    im2col_f16
+    mul_mat_Ab_Bi_8x4
+    mul_mv_f16_f16
+    mul_mv_f16_f32_1row
+    mul_mv_f16_f32_l4
+    mul_mv_f16_f32
+    mul_mv_f32_f32
+    mul_mv_q4_0_f32
+    mul_mv_q4_0_f32_v
+    mul_mv_q4_0_f32_8x_flat
+    mul_mv_q4_0_f32_1d_8x_flat
+    mul_mv_q4_0_f32_1d_16x_flat
+    mul_mv_q6_k
+    mul
+    norm
+    relu
+    rms_norm
+    rope
+    scale
+    silu
+    softmax_4_f32
+    softmax_4_f16
+    softmax_f32
+    softmax_f16
+    transpose
 )
 
 foreach (K ${GGML_OPENCL_KERNELS})

ggml/src/ggml-opencl/kernels/add.cl

@@ -0,0 +1,83 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+//------------------------------------------------------------------------------
+// add
+//------------------------------------------------------------------------------
+
+// general-purpose kernel for addition of two tensors
+// pros: works for non-contiguous tensors, supports broadcast across dims 1, 2 and 3
+// cons: not very efficient
+kernel void kernel_add(
+        global char * src0,
+        ulong  offset0,
+        global char * src1,
+        ulong  offset1,
+        global char * dst,
+        ulong  offsetd,
+        int   ne00,
+        int   ne01,
+        int   ne02,
+        int   ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int   ne10,
+        int   ne11,
+        int   ne12,
+        int   ne13,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int   ne0,
+        int   ne1,
+        int   ne2,
+        int   ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+    src0 = src0 + offset0;
+    src1 = src1 + offset1;
+    dst = dst + offsetd;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int i13 = i03 % ne13;
+    int i12 = i02 % ne12;
+    int i11 = i01 % ne11;
+
+    global char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01;
+    global char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11;
+    global char * dst_ptr  = dst  + i03*nb3  + i02*nb2  + i01*nb1;
+
+    for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
+        const int i10 = i0 % ne10;
+        *((global float *)(dst_ptr + i0*nb0)) = *((global float *)(src0_ptr + i0*nb00)) + *((global float *)(src1_ptr + i10*nb10));
+    }
+}
+
+// assumption: src1 is a row
+// broadcast src1 into src0
+kernel void kernel_add_row(
+        global float4 * src0,
+        ulong  offset0,
+        global float4 * src1,
+        ulong  offset1,
+        global float4 * dst,
+        ulong  offsetd,
+        int ne
+) {
+    src0 = (global float4*)((global char*)src0 + offset0);
+    src1 = (global float4*)((global char*)src1 + offset1);
+    dst = (global float4*)((global char*)dst + offsetd);
+
+    // This performs better than using %.
+    uint gid = get_global_id(0);
+    uint idx1 = gid - (gid/ne)*ne; // get_global_id(0) % ne
+    dst[gid] = src0[gid] + src1[idx1];
+}

ggml/src/ggml-opencl/kernels/clamp.cl

@@ -0,0 +1,20 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+//------------------------------------------------------------------------------
+// clamp
+//------------------------------------------------------------------------------
+kernel void kernel_clamp(
+        global float * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd,
+        float min,
+        float max
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    dst[get_global_id(0)] = src0[get_global_id(0)] < min ?
+        min :
+        (src0[get_global_id(0)] > max ? max : src0[get_global_id(0)]);
+}

ggml/src/ggml-opencl/kernels/cpy.cl

@@ -0,0 +1,184 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+//------------------------------------------------------------------------------
+// cpy
+//------------------------------------------------------------------------------
+
+kernel void kernel_cpy_f16_f16(
+        global half * src0,
+        ulong offset0,
+        global half * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+    src0 = (global half*)((global char*)src0 + offset0);
+    dst = (global half*)((global char*)dst + offsetd);
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+
+    int i3 = n / (ne2*ne1*ne0);
+    int i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
+    int i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
+    int i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
+
+    global half * dst_data = (global half *) ((global char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        global const half * src = (global half *)((global char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
+        dst_data[i00] = src[0];
+    }
+}
+
+kernel void kernel_cpy_f16_f32(
+        global half * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+
+    src0 = (global half*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+
+    int i3 = n / (ne2*ne1*ne0);
+    int i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
+    int i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
+    int i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
+
+    global float * dst_data = (global float *) ((global char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        global half * src = (global half *)((global char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
+        dst_data[i00] = src[0];
+    }
+}
+
+kernel void kernel_cpy_f32_f16(
+        global float * src0,
+        ulong offset0,
+        global half * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    dst = (global half*)((global char*)dst + offsetd);
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+
+    int i3 = n / (ne2*ne1*ne0);
+    int i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
+    int i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
+    int i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
+
+    global half * dst_data = (global half *) ((global char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        global const float * src = (global float *)((global char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
+
+        dst_data[i00] = src[0];
+    }
+}
+
+kernel void kernel_cpy_f32_f32(
+        global float * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+
+    int i3 = n / (ne2*ne1*ne0);
+    int i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
+    int i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
+    int i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
+
+    global float * dst_data = (global float *) ((global char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        global const float * src = (global float *)((global char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
+
+        dst_data[i00] = src[0];
+    }
+}

ggml/src/ggml-opencl/kernels/cvt.cl

@@ -1,39 +1,20 @@
 //------------------------------------------------------------------------------
-// This file is contains additional kernels for data conversion.
+// This file is contains kernels for data conversion.
 // These kernels are used when loading the model, so its performance is less
 // important.
 //------------------------------------------------------------------------------
-#ifdef cl_khr_fp16
 #pragma OPENCL EXTENSION cl_khr_fp16 : enable
-#elif defined(cl_amd_fp16)
-#pragma OPENCL EXTENSION cl_amd_fp16 : enable
-#else
-#error "Half precision floating point not supportedby OpenCL implementation on your device."
-#endif
-
-#ifdef cl_khr_subgroups
-#pragma OPENCL EXTENSION cl_khr_subgroups : enable
-#elif defined(cl_intel_subgroups)
-#pragma OPENCL EXTENSION cl_intel_subgroups : enable
-#else
-#error "Subgroup not supported on your device."
-#endif
 
 #ifdef cl_intel_required_subgroup_size
-// Always use subgroup size of 32 on Intel.
 #pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
 #define INTEL_GPU 1
 #define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
 #define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
 #elif defined(cl_qcom_reqd_sub_group_size)
-// Always use subgroups size of 64 on Adreno.
 #pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
 #define ADRENO_GPU 1
 #define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
 #define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
-#else
-// TODO: do not know how to choose subgroup size on other GPUs.
-#error "Selecting subgroup size is not supported on your device."
 #endif
 
 #define QK4_0                   32
@@ -66,13 +47,44 @@ struct block_q4_0
 };
 
 //------------------------------------------------------------------------------
-// mul_vec_q_n_f32_flat_noshuffle
-//
-// This variation uses flat arrays (struct of arrays, SOA) representation for
-// quant tensors. It also uses non shuffled bit order for weights.
-//
-// The shuffled version is kept in the original file because moving it here
-// seems to result in worse performance for adreno.
+// kernel_convert_block_q4_0
+// Convert the block_q4_0 format to 2 separate arrays (AOS -> SOA).
+// This kernel does not deshuffle the bits.
+//------------------------------------------------------------------------------
+kernel void kernel_convert_block_q4_0(
+    global struct block_q4_0 * src0,
+    global uchar * dst_q,
+    global half  * dst_d
+) {
+    global struct block_q4_0 * b = (global struct block_q4_0 *) src0 + get_global_id(0);
+    global uchar * q = (global uchar *) dst_q + QK4_0/2*get_global_id(0);
+    global half  * d = (global half *) dst_d + get_global_id(0);
+
+    *d = b->d;
+
+    for (int i = 0; i < QK4_0/2; ++i) {
+        q[i] = b->qs[i];
+    }
+}
+
+kernel void kernel_restore_block_q4_0(
+    global uchar * src_q,
+    global half  * src_d,
+    global struct block_q4_0 * dst
+) {
+    global struct block_q4_0 * b = (global struct block_q4_0 *) dst + get_global_id(0);
+    global uchar * q = (global uchar *) src_q + QK4_0/2*get_global_id(0);
+    global half  * d = (global half *) src_d + get_global_id(0);
+
+    b->d = *d;
+    for (int i = 0; i < QK4_0/2; ++i) {
+        b->qs[i] = q[i];
+    }
+}
+
+//------------------------------------------------------------------------------
+// kernel_convert_block_q4_0_noshuffle
+// Flatten q4_0 weights and unshuffle the bits
 //------------------------------------------------------------------------------
 
 kernel void kernel_convert_block_q4_0_noshuffle(

ggml/src/ggml-opencl/kernels/diag_mask_inf.cl

@@ -0,0 +1,58 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+//------------------------------------------------------------------------------
+// diag_mask_inf kernels
+//------------------------------------------------------------------------------
+kernel void kernel_diag_mask_inf(
+        global float * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int n_past
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i02 = get_global_id(2);
+    int i01 = get_global_id(1);
+    int i00 = get_global_id(0);
+
+    if (i00 > n_past + i01) {
+        dst[i02*ne01*ne00 + i01*ne00 + i00] = -INFINITY;
+    } else {
+        dst[i02*ne01*ne00 + i01*ne00 + i00] = src0[i02*ne01*ne00 + i01*ne00 + i00];
+    }
+}
+
+kernel void kernel_diag_mask_inf_8(
+        global float4 * src0,
+        ulong offset0,
+        global float4 * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int n_past
+) {
+    src0 = (global float4*)((global char*)src0 + offset0);
+    dst = (global float4*)((global char*)dst + offsetd);
+
+    int i = 2*get_global_id(0);
+
+    dst[i+0] = src0[i+0];
+    dst[i+1] = src0[i+1];
+    int i4 = 4*i;
+    int i02 = i4/(ne00*ne01); i4 -= i02*ne00*ne01;
+    int i01 = i4/(ne00);      i4 -= i01*ne00;
+    int i00 = i4;
+    for (int k = 3; k >= 0; --k) {
+        if (i00 + 4 + k <= n_past + i01) {
+            break;
+        }
+        (&dst[i+1])[k] = -INFINITY;
+        if (i00 + k > n_past + i01) {
+            (&dst[i])[k] = -INFINITY;
+        }
+    }
+}

ggml/src/ggml-opencl/kernels/gelu.cl

@@ -0,0 +1,62 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+//------------------------------------------------------------------------------
+// gelu
+//------------------------------------------------------------------------------
+#define GELU_COEF_A     0.044715f
+#define GELU_QUICK_COEF -1.702f
+#define SQRT_2_OVER_PI  0.79788456080286535587989211986876f
+
+kernel void kernel_gelu(
+    global float * src0,
+    ulong offset0,
+    global float * dst,
+    ulong offsetd
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    float x = src0[get_global_id(0)];
+
+    dst[get_global_id(0)] = 0.5f*x*(1.0f + tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
+}
+
+kernel void kernel_gelu_4(
+    global float4 * src0,
+    ulong offset0,
+    global float4 * dst,
+    ulong offsetd
+) {
+    src0 = (global float4*)((global char*)src0 + offset0);
+    dst = (global float4*)((global char*)dst + offsetd);
+
+    float4 x = src0[get_global_id(0)];
+
+    dst[get_global_id(0)] = 0.5f*x*(1.0f + tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
+}
+
+kernel void kernel_gelu_quick(
+    global float * src0,
+    ulong offset0,
+    global float * dst,
+    ulong offsetd
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    float x = src0[get_global_id(0)];
+    dst[get_global_id(0)] = x*(1.0f/(1.0f+exp(GELU_QUICK_COEF*x)));
+}
+
+kernel void kernel_gelu_quick_4(
+    global float4 * src0,
+    ulong offset0,
+    global float4 * dst,
+    ulong offsetd
+) {
+    src0 = (global float4*)((global char*)src0 + offset0);
+    dst = (global float4*)((global char*)dst + offsetd);
+
+    float4 x = src0[get_global_id(0)];
+    dst[get_global_id(0)] = x*(1.0f/(1.0f+exp(GELU_QUICK_COEF*x)));
+}

ggml/src/ggml-opencl/kernels/get_rows.cl

@@ -0,0 +1,163 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+typedef char int8_t;
+typedef uchar uint8_t;
+typedef short int16_t;
+typedef ushort uint16_t;
+typedef int int32_t;
+typedef uint uint32_t;
+
+#define QK4_0                   32
+
+//------------------------------------------------------------------------------
+// block_q4_0
+//------------------------------------------------------------------------------
+struct block_q4_0
+{
+    half d;
+    uint8_t qs[QK4_0 / 2];
+};
+
+
+//------------------------------------------------------------------------------
+// dequantize_q4_0_f32, dequantize_q4_0_f16
+//------------------------------------------------------------------------------
+void dequantize_q4_0_f32(global struct block_q4_0 * xb, short il, float16 * reg) {
+    global ushort * qs = ((global ushort *)xb + 1);
+    float d1 = il ? (xb->d / 16.h) : xb->d;
+    float d2 = d1 / 256.f;
+    float md = -8.h * xb->d;
+    ushort mask0 = il ? 0x00F0 : 0x000F;
+    ushort mask1 = mask0 << 8;
+
+    reg->s0 = d1 * (qs[0] & mask0) + md;
+    reg->s1 = d2 * (qs[0] & mask1) + md;
+
+    reg->s2 = d1 * (qs[1] & mask0) + md;
+    reg->s3 = d2 * (qs[1] & mask1) + md;
+
+    reg->s4 = d1 * (qs[2] & mask0) + md;
+    reg->s5 = d2 * (qs[2] & mask1) + md;
+
+    reg->s6 = d1 * (qs[3] & mask0) + md;
+    reg->s7 = d2 * (qs[3] & mask1) + md;
+
+    reg->s8 = d1 * (qs[4] & mask0) + md;
+    reg->s9 = d2 * (qs[4] & mask1) + md;
+
+    reg->sa = d1 * (qs[5] & mask0) + md;
+    reg->sb = d2 * (qs[5] & mask1) + md;
+
+    reg->sc = d1 * (qs[6] & mask0) + md;
+    reg->sd = d2 * (qs[6] & mask1) + md;
+
+    reg->se = d1 * (qs[7] & mask0) + md;
+    reg->sf = d2 * (qs[7] & mask1) + md;
+}
+
+
+//------------------------------------------------------------------------------
+// get_rows
+//------------------------------------------------------------------------------
+kernel void kernel_get_rows_f32(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        ulong nb01,
+        ulong nb02,
+        int ne10,
+        ulong nb10,
+        ulong nb11,
+        ulong nb1,
+        ulong nb2
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i10 = get_group_id(0);
+    int i11 = get_group_id(1);
+
+    int r = ((global int *) ((global char *) src1 + i11*nb11 + i10*nb10))[0];
+
+    int i02 = i11;
+
+    for (int ind = get_local_id(0); ind < ne00; ind += get_local_size(0)) {
+        ((global float *) ((global char *) dst + i11*nb2 + i10*nb1))[ind] =
+            ((global float *) ((global char *) src0 + r*nb01 + i02*nb02))[ind];
+    }
+}
+
+kernel void kernel_get_rows_f16(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        ulong nb01,
+        ulong nb02,
+        int ne10,
+        ulong nb10,
+        ulong nb11,
+        ulong nb1,
+        ulong nb2
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i10 = get_group_id(0);
+    int i11 = get_group_id(1);
+
+    int r = ((global int32_t *) ((global char *) src1 + i11*nb11 + i10*nb10))[0];
+
+    int i02 = i11;
+
+    for (int ind = get_local_id(0); ind < ne00; ind += get_local_size(0)) {
+        ((global float *) ((global char *) dst + i11*nb2 + i10*nb1))[ind] =
+            ((global half *) ((global char *) src0 + r*nb01 + i02*nb02))[ind];
+    }
+}
+
+kernel void kernel_get_rows_q4_0(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        ulong nb01,
+        ulong nb02,
+        int ne10,
+        ulong nb10,
+        ulong nb11,
+        ulong nb1,
+        ulong nb2
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    const int NL = 2;
+
+    int i10 = get_group_id(0);
+    int i11 = get_group_id(1);
+
+    int r = ((global int32_t *) ((global char *) src1 + i11*nb11 + i10*nb10))[0];
+
+    int i02 = i11;
+
+    for (int ind = get_local_id(0); ind < ne00/16; ind += get_local_size(0)) {
+        float16 temp;
+        dequantize_q4_0_f32(
+            ((global struct block_q4_0 *) ((global char *) src0 + r*nb01 + i02*nb02)) + ind/NL, ind%NL, &temp);
+        *(((global float16 *) ((global char *) dst + i11*nb2 + i10*nb1)) + ind) = temp;
+    }
+}

ggml/src/ggml-opencl/kernels/ggml-opencl_im2col.cl

@@ -1,146 +0,0 @@
-#ifdef cl_khr_fp16
-#pragma OPENCL EXTENSION cl_khr_fp16 : enable
-#elif defined(cl_amd_fp16)
-#pragma OPENCL EXTENSION cl_amd_fp16 : enable
-#else
-#error "Half precision floating point not supportedby OpenCL implementation on your device."
-#endif
-
-#ifdef cl_khr_subgroups
-#pragma OPENCL EXTENSION cl_khr_subgroups : enable
-#elif defined(cl_intel_subgroups)
-#pragma OPENCL EXTENSION cl_intel_subgroups : enable
-#else
-#error "Subgroup not supported on your device."
-#endif
-
-#ifdef cl_intel_required_subgroup_size
-// Always use subgroup size of 32 on Intel.
-#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
-#define INTEL_GPU 1
-#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
-#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
-#elif defined(cl_qcom_reqd_sub_group_size)
-// Always use subgroups size of 64 on Adreno.
-#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
-#define ADRENO_GPU 1
-#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
-#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
-#else
-// TODO: do not know how to choose subgroup size on other GPUs.
-#error "Selecting subgroup size is not supported on your device."
-#endif
-
-kernel void kernel_im2col_f32(
-        global float * src1,
-        ulong offset1,
-        global float * dst,
-        ulong offsetd,
-        ulong batch_offset,
-        ulong delta_offset,
-        long IW,
-        long IH,
-        long IC,
-        long OW,
-        long OH,
-        long KW,
-        long KH,
-        long pelements,
-        long CHW,
-        int  s0,
-        int  s1,
-        int  p0,
-        int  p1,
-        int  d0,
-        int  d1
-) {
-    // threadIdx.x + blockIdx.x * blockDim.x
-    long i = get_global_id(0);
-    if (i >= pelements) {
-        return;
-    }
-
-    src1 = (global float*)((global char*)src1 + offset1);
-    dst = (global float*)((global char*)dst + offsetd);
-
-    long  ksize = OW * (KH > 1 ? KW : 1);
-    long  kx = i / ksize;
-    long  kd = kx * ksize;
-    long  ky = (i - kd) / OW;
-    long  ix = i % OW;
-
-    long  oh = get_group_id(1);
-    long  batch = get_group_id(2) / IC;
-    long  ic = get_group_id(2) % IC;
-
-    long iiw = ix * s0 + kx * d0 - p0;
-    long iih = oh * s1 + ky * d1 - p1;
-
-    long offset_dst =
-        ((batch * OH + oh) * OW + ix) * CHW +
-        (ic * (KW * KH) + ky * KW + kx);
-
-    if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
-        dst[offset_dst] = 0.0f;
-    } else {
-        long offset_src = ic * delta_offset + batch * batch_offset;
-        dst[offset_dst] = src1[offset_src + iih * IW + iiw];
-    }
-}
-
-kernel void kernel_im2col_f16(
-        global float * src1,
-        ulong offset1,
-        global half  * dst,
-        ulong offsetd,
-        ulong batch_offset,
-        ulong delta_offset,
-        long IW,
-        long IH,
-        long IC,
-        long OW,
-        long OH,
-        long KW,
-        long KH,
-        long pelements,
-        long CHW,
-        int  s0,
-        int  s1,
-        int  p0,
-        int  p1,
-        int  d0,
-        int  d1
-) {
-    long i = get_global_id(0);
-
-    if (i >= pelements) {
-        return;
-    }
-
-    src1 = (global float*)((global char*)src1 + offset1);
-    dst = (global half*)((global char*)dst + offsetd);
-
-    long  ksize = OW * (KH > 1 ? KW : 1);
-    long  kx = i / ksize;
-    long  kd = kx * ksize;
-    long  ky = (i - kd) / OW;
-    long  ix = i % OW;
-
-    long  oh = get_group_id(1);
-    long  batch = get_group_id(2) / IC;
-    long  ic = get_group_id(2) % IC;
-
-    long iiw = ix * s0 + kx * d0 - p0;
-    long iih = oh * s1 + ky * d1 - p1;
-
-    long offset_dst =
-        ((batch * OH + oh) * OW + ix) * CHW +
-        (ic * (KW * KH) + ky * KW + kx);
-
-    if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
-        dst[offset_dst] = 0.0f;
-    } else {
-        long offset_src = ic * delta_offset + batch * batch_offset;
-        dst[offset_dst] = src1[offset_src + iih * IW + iiw];
-    }
-}

ggml/src/ggml-opencl/kernels/ggml-opencl_transpose_16.cl

@@ -1,26 +0,0 @@
-// 16-bit transpose, loading/storing a 4x4 tile of elements
-
-#pragma OPENCL EXTENSION cl_khr_fp16 : enable
-
-kernel void kernel_transpose_16(
-    __read_only image1d_buffer_t input,
-    __write_only image1d_buffer_t output,
-    const uint rows,
-    const uint cols
-) {
-
-    const int i = get_global_id(0);
-    const int j = get_global_id(1);
-    const int i_2 = i<<2;
-    const int j_2 = j<<2;
-
-    half4 temp0 = read_imageh(input, (j_2+0)*cols+i);
-    half4 temp1 = read_imageh(input, (j_2+1)*cols+i);
-    half4 temp2 = read_imageh(input, (j_2+2)*cols+i);
-    half4 temp3 = read_imageh(input, (j_2+3)*cols+i);
-
-    write_imageh(output, (i_2+0)*rows+j, (half4)(temp0.s0, temp1.s0, temp2.s0, temp3.s0));
-    write_imageh(output, (i_2+1)*rows+j, (half4)(temp0.s1, temp1.s1, temp2.s1, temp3.s1));
-    write_imageh(output, (i_2+2)*rows+j, (half4)(temp0.s2, temp1.s2, temp2.s2, temp3.s2));
-    write_imageh(output, (i_2+3)*rows+j, (half4)(temp0.s3, temp1.s3, temp2.s3, temp3.s3));
-}

ggml/src/ggml-opencl/kernels/ggml-opencl_transpose_32.cl

@@ -1,25 +0,0 @@
-// 32-bit transpose, loading/storing a 4x4 tile of elements
-
-kernel void kernel_transpose_32(
-    __read_only image1d_buffer_t input,
-    __write_only image1d_buffer_t output,
-    const uint rows,
-    const uint cols
-) {
-
-    const int i = get_global_id(0);
-    const int j = get_global_id(1);
-    const int i_2 = i<<2;
-    const int j_2 = j<<2;
-
-    float4 temp0 = read_imagef(input, (j_2+0)*cols+i);
-    float4 temp1 = read_imagef(input, (j_2+1)*cols+i);
-    float4 temp2 = read_imagef(input, (j_2+2)*cols+i);
-    float4 temp3 = read_imagef(input, (j_2+3)*cols+i);
-
-    write_imagef(output, (i_2+0)*rows+j, (float4)(temp0.s0, temp1.s0, temp2.s0, temp3.s0));
-    write_imagef(output, (i_2+1)*rows+j, (float4)(temp0.s1, temp1.s1, temp2.s1, temp3.s1));
-    write_imagef(output, (i_2+2)*rows+j, (float4)(temp0.s2, temp1.s2, temp2.s2, temp3.s2));
-    write_imagef(output, (i_2+3)*rows+j, (float4)(temp0.s3, temp1.s3, temp2.s3, temp3.s3));
-
-}

ggml/src/ggml-opencl/kernels/ggml-opencl_transpose_32_16.cl

@@ -1,35 +0,0 @@
-// 32-bit transpose, loading/storing a 4x4 tile of elements
-// Only used for activations
-// converts to FP16
-// also adds zero padding for non multiple of 8 prompt lengths
-#pragma OPENCL EXTENSION cl_khr_fp16 : enable
-
-kernel void kernel_transpose_32_16(__read_only image1d_buffer_t input, __write_only image1d_buffer_t output, const uint rows, const uint cols, const uint padded_rows) {
-
-    const int i = get_global_id(0);
-    const int j = get_global_id(1);
-    const int i_2 = i<<2;
-    const int j_2 = j<<2;
-    half4 temp0 = {0,0,0,0}; // initialize outputs to 0
-    half4 temp1 = {0,0,0,0};
-    half4 temp2 = {0,0,0,0};
-    half4 temp3 = {0,0,0,0};
-
-    if((j_2+0)*cols+i*4+3 < rows*cols*16){ // only load from a valid location. Otherwise keep register data as 0
-        temp0 = read_imageh(input, (j_2+0)*cols+i);
-    }
-    if((j_2+1)*cols+i*4+3 < rows*cols*16){
-        temp1 = read_imageh(input, (j_2+1)*cols+i);
-    }
-    if((j_2+2)*cols+i*4+3 < rows*cols*16){
-        temp2 = read_imageh(input, (j_2+2)*cols+i);
-    }
-    if((j_2+3)*cols+i*4+3 < rows*cols*16){
-        temp3 = read_imageh(input, (j_2+3)*cols+i);
-    }
-
-    write_imageh(output, (i_2+0)*padded_rows+j, (half4)(temp0.s0, temp1.s0, temp2.s0, temp3.s0)); // no conditionals for output, includes zero padding
-    write_imageh(output, (i_2+1)*padded_rows+j, (half4)(temp0.s1, temp1.s1, temp2.s1, temp3.s1));
-    write_imageh(output, (i_2+2)*padded_rows+j, (half4)(temp0.s2, temp1.s2, temp2.s2, temp3.s2));
-    write_imageh(output, (i_2+3)*padded_rows+j, (half4)(temp0.s3, temp1.s3, temp2.s3, temp3.s3));
-}

ggml/src/ggml-opencl/kernels/im2col_f16.cl

@@ -0,0 +1,57 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+kernel void kernel_im2col_f16(
+        global float * src1,
+        ulong offset1,
+        global half  * dst,
+        ulong offsetd,
+        ulong batch_offset,
+        ulong delta_offset,
+        long IW,
+        long IH,
+        long IC,
+        long OW,
+        long OH,
+        long KW,
+        long KH,
+        long pelements,
+        long CHW,
+        int  s0,
+        int  s1,
+        int  p0,
+        int  p1,
+        int  d0,
+        int  d1
+) {
+    long i = get_global_id(0);
+    if (i >= pelements) {
+        return;
+    }
+
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global half*)((global char*)dst + offsetd);
+
+    long  ksize = OW * (KH > 1 ? KW : 1);
+    long  kx = i / ksize;
+    long  kd = kx * ksize;
+    long  ky = (i - kd) / OW;
+    long  ix = i % OW;
+
+    long  oh = get_group_id(1);
+    long  batch = get_group_id(2) / IC;
+    long  ic = get_group_id(2) % IC;
+
+    long iiw = ix * s0 + kx * d0 - p0;
+    long iih = oh * s1 + ky * d1 - p1;
+
+    long offset_dst =
+        ((batch * OH + oh) * OW + ix) * CHW +
+        (ic * (KW * KH) + ky * KW + kx);
+
+    if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
+        dst[offset_dst] = 0.0f;
+    } else {
+        long offset_src = ic * delta_offset + batch * batch_offset;
+        dst[offset_dst] = src1[offset_src + iih * IW + iiw];
+    }
+}

ggml/src/ggml-opencl/kernels/im2col_f32.cl

@@ -0,0 +1,57 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+kernel void kernel_im2col_f32(
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        ulong batch_offset,
+        ulong delta_offset,
+        long IW,
+        long IH,
+        long IC,
+        long OW,
+        long OH,
+        long KW,
+        long KH,
+        long pelements,
+        long CHW,
+        int  s0,
+        int  s1,
+        int  p0,
+        int  p1,
+        int  d0,
+        int  d1
+) {
+    long i = get_global_id(0);
+    if (i >= pelements) {
+        return;
+    }
+
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    long  ksize = OW * (KH > 1 ? KW : 1);
+    long  kx = i / ksize;
+    long  kd = kx * ksize;
+    long  ky = (i - kd) / OW;
+    long  ix = i % OW;
+
+    long  oh = get_group_id(1);
+    long  batch = get_group_id(2) / IC;
+    long  ic = get_group_id(2) % IC;
+
+    long iiw = ix * s0 + kx * d0 - p0;
+    long iih = oh * s1 + ky * d1 - p1;
+
+    long offset_dst =
+        ((batch * OH + oh) * OW + ix) * CHW +
+        (ic * (KW * KH) + ky * KW + kx);
+
+    if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
+        dst[offset_dst] = 0.0f;
+    } else {
+        long offset_src = ic * delta_offset + batch * batch_offset;
+        dst[offset_dst] = src1[offset_src + iih * IW + iiw];
+    }
+}

ggml/src/ggml-opencl/kernels/mul.cl

@@ -0,0 +1,79 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+//------------------------------------------------------------------------------
+// mul
+//------------------------------------------------------------------------------
+kernel void kernel_mul(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global char * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        int ne13,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+    src0 = src0 + offset0;
+    src1 = src1 + offset1;
+    dst  = dst + offsetd;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int i13 = i03 % ne13;
+    int i12 = i02 % ne12;
+    int i11 = i01 % ne11;
+
+    global char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01;
+    global char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11;
+    global char * dst_ptr  = dst  + i03*nb3  + i02*nb2  + i01*nb1;
+
+    for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
+        const int i10 = i0 % ne10;
+        *((global float *)(dst_ptr + i0*nb0)) = *((global float *)(src0_ptr + i0*nb00)) * *((global float *)(src1_ptr + i10*nb10));
+    }
+}
+
+// assumption: src1 is a row
+// broadcast src1 into src0
+kernel void kernel_mul_row(
+        global float4 * src0,
+        ulong offset0,
+        global float4 * src1,
+        ulong offset1,
+        global float4 * dst,
+        ulong offsetd,
+        int ne
+) {
+    src0 = (global float4*)((global char*)src0 + offset0);
+    src1 = (global float4*)((global char*)src1 + offset1);
+    dst = (global float4*)((global char*)dst + offsetd);
+
+    // This performs better than using %.
+    uint gid = get_global_id(0);
+    uint idx1 = gid - (gid/ne)*ne; // get_global_id(0) % ne
+    dst[gid] = src0[gid] * src1[idx1];
+}

ggml/src/ggml-opencl/kernels/mul_mv_f16_f16.cl

@@ -0,0 +1,118 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define N_F16_F16 4
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_f16_f16(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3)
+{
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int r0 = get_group_id(0);
+    int rb = get_group_id(1)*N_F16_F16;
+    int im = get_group_id(2);
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset_src0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
+
+    global half * x = (global half *) (src0 + offset_src0);
+
+    if (ne00 < 128) {
+        for (int row = 0; row < N_F16_F16; ++row) {
+            int r1 = rb + row;
+            if (r1 >= ne11) {
+                break;
+            }
+
+            ulong offset_src1 = r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+            global half * y = (global half *) (src1 + offset_src1);
+
+            float sumf = 0;
+            for (int i = get_sub_group_local_id(); i < ne00; i += get_max_sub_group_size()) {
+                sumf += (half) x[i] * (half) y[i];
+            }
+
+            float all_sum = sub_group_reduce_add(sumf);
+            if (get_sub_group_local_id() == 0) {
+                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+            }
+        }
+    } else {
+        global half4 * x4 = (global half4 *)x;
+        for (int row = 0; row < N_F16_F16; ++row) {
+            int r1 = rb + row;
+            if (r1 >= ne11) {
+                break;
+            }
+
+            ulong offset_src1 = r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+            global half  * y  = (global half  *) (src1 + offset_src1);
+            global half4 * y4 = (global half4 *) y;
+
+            float sumf = 0;
+            for (int i = get_sub_group_local_id(); i < ne00/4; i += get_max_sub_group_size()) {
+                sumf += (half) x4[i].s0 * y4[i].s0;
+                sumf += (half) x4[i].s1 * y4[i].s1;
+                sumf += (half) x4[i].s2 * y4[i].s2;
+                sumf += (half) x4[i].s3 * y4[i].s3;
+            }
+
+            float all_sum = sub_group_reduce_add(sumf);
+            if (get_sub_group_local_id() == 0) {
+                for (int i = 4*(ne00/4); i < ne00; ++i) {
+                    all_sum += (half) x[i] * y[i];
+                }
+                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+            }
+        }
+    }
+}

ggml/src/ggml-opencl/kernels/mul_mv_f16_f32.cl

@@ -0,0 +1,118 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define N_F16_F32 4
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_f16_f32(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int r0 = get_group_id(0);
+    int rb = get_group_id(1)*N_F16_F32;
+    int im = get_group_id(2);
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset_src0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
+
+    global half * x = (global half *) (src0 + offset_src0);
+
+    if (ne00 < 128) {
+        for (int row = 0; row < N_F16_F32; ++row) {
+            int r1 = rb + row;
+            if (r1 >= ne11) {
+                break;
+            }
+
+            ulong offset_src1 = r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+            global float * y = (global float *) (src1 + offset_src1);
+
+            float sumf = 0;
+            for (int i = get_sub_group_local_id(); i < ne00; i += get_max_sub_group_size()) {
+                sumf += convert_float(x[i]) * y[i];
+            }
+
+            float all_sum = sub_group_reduce_add(sumf);
+            if (get_sub_group_local_id() == 0) {
+                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+            }
+        }
+    } else {
+        global half4 * x4 = (global half4 *)x;
+        for (int row = 0; row < N_F16_F32; ++row) {
+            int r1 = rb + row;
+            if (r1 >= ne11) {
+                break;
+            }
+
+            ulong offset_src1 = r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+            global float  * y  = (global float  *) (src1 + offset_src1);
+            global float4 * y4 = (global float4 *) y;
+
+            float sumf = 0;
+            for (int i = get_sub_group_local_id(); i < ne00/4; i += get_max_sub_group_size()) {
+                sumf += convert_float(x4[i].s0) * y4[i].s0;
+                sumf += convert_float(x4[i].s1) * y4[i].s1;
+                sumf += convert_float(x4[i].s2) * y4[i].s2;
+                sumf += convert_float(x4[i].s3) * y4[i].s3;
+            }
+
+            float all_sum = sub_group_reduce_add(sumf);
+            if (get_sub_group_local_id() == 0) {
+                for (int i = 4*(ne00/4); i < ne00; ++i) {
+                    all_sum += (float) x[i] * y[i];
+                }
+                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+            }
+        }
+    }
+}

ggml/src/ggml-opencl/kernels/mul_mv_f16_f32_1row.cl

@@ -0,0 +1,94 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_f16_f32_1row(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset_src0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
+    ulong offset_src1 = r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+    global half  * x = (global half  *) (src0 + offset_src0);
+    global float * y = (global float *) (src1 + offset_src1);
+
+    float sumf = 0;
+    if (ne00 < 128) {
+        for (int i = get_sub_group_local_id(); i < ne00; i += get_max_sub_group_size()) {
+            sumf += (float) x[i] * (float) y[i];
+        }
+        float all_sum = sub_group_reduce_add(sumf);
+        if (get_sub_group_local_id() == 0) {
+            dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+        }
+    } else {
+        global half4  * x4 = (global half4  *) x;
+        global float4 * y4 = (global float4 *) y;
+        for (int i = get_sub_group_local_id(); i < ne00/4; i += get_max_sub_group_size()) {
+            sumf += (float) x4[i].s0 * y4[i].s0;
+            sumf += (float) x4[i].s1 * y4[i].s1;
+            sumf += (float) x4[i].s2 * y4[i].s2;
+            sumf += (float) x4[i].s3 * y4[i].s3;
+        }
+        float all_sum = sub_group_reduce_add(sumf);
+        if (get_sub_group_local_id() == 0) {
+            for (int i = 4*(ne00/4); i < ne00; ++i) {
+                all_sum += (float) x[i] * y[i];
+            }
+            dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+        }
+    }
+
+}

ggml/src/ggml-opencl/kernels/mul_mv_f16_f32_l4.cl

@@ -0,0 +1,84 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+// Assumes row size (ne00) is a multiple of 4
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_f16_f32_l4(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int nrows = ne11;
+    int r0 = get_group_id(0);
+    int im = get_group_id(2);
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset_src0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
+
+    global half4 * x4 = (global half4 *) (src0 + offset_src0);
+
+    for (int r1 = 0; r1 < nrows; ++r1) {
+        ulong offset_src1 = r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+        global float4 * y4 = (global float4 *) (src1 + offset_src1);
+
+        float sumf = 0;
+        for (int i = get_sub_group_local_id(); i < ne00/4; i += get_max_sub_group_size()) {
+            sumf += convert_float(x4[i].s0) * y4[i].s0;
+            sumf += convert_float(x4[i].s1) * y4[i].s1;
+            sumf += convert_float(x4[i].s2) * y4[i].s2;
+            sumf += convert_float(x4[i].s3) * y4[i].s3;
+        }
+
+        float all_sum = sub_group_reduce_add(sumf);
+        if (get_sub_group_local_id() == 0) {
+            dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+        }
+    }
+}

ggml/src/ggml-opencl/kernels/mul_mv_f32_f32.cl

@@ -0,0 +1,118 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define N_F32_F32 4
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_f32_f32(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int r0 = get_group_id(0);
+    int rb = get_group_id(1)*N_F32_F32;
+    int im = get_group_id(2);
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset_src0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
+
+    global float * x = (global float *) (src0 + offset_src0);
+
+    if (ne00 < 128) {
+        for (int row = 0; row < N_F32_F32; ++row) {
+            int r1 = rb + row;
+            if (r1 >= ne11) {
+                break;
+            }
+
+            ulong offset_src1 = r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+            global float * y = (global float *) (src1 + offset_src1);
+
+            float sumf = 0;
+            for (int i = get_sub_group_local_id(); i < ne00; i += get_max_sub_group_size()) {
+                sumf += (float) x[i] * (float) y[i];
+            }
+
+            float all_sum = sub_group_reduce_add(sumf);
+            if (get_sub_group_local_id() == 0) {
+                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+            }
+        }
+    } else {
+        global float4 * x4 = (global float4 *)x;
+        for (int row = 0; row < N_F32_F32; ++row) {
+            int r1 = rb + row;
+            if (r1 >= ne11) {
+                break;
+            }
+
+            ulong offset_src1 = r1*nb11 + (i12   )*nb12 + (i13   )*nb13;
+
+            global float  * y  = (global float  *) (src1 + offset_src1);
+            global float4 * y4 = (global float4 *) y;
+
+            float sumf = 0;
+            for (int i = get_sub_group_local_id(); i < ne00/4; i += get_max_sub_group_size()) {
+                sumf += (float) x4[i].s0 * y4[i].s0;
+                sumf += (float) x4[i].s1 * y4[i].s1;
+                sumf += (float) x4[i].s2 * y4[i].s2;
+                sumf += (float) x4[i].s3 * y4[i].s3;
+            }
+
+            float all_sum = sub_group_reduce_add(sumf);
+            if (get_sub_group_local_id() == 0) {
+                for (int i = 4*(ne00/4); i < ne00; ++i) {
+                    all_sum += (float) x[i] * y[i];
+                }
+                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
+            }
+        }
+    }
+}

ggml/src/ggml-opencl/kernels/mul_mv_q4_0_f32.cl

@@ -0,0 +1,192 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define QK4_0                   32
+#define QR4_0                   2
+#define QK4_1                   32
+#define QR4_1                   2
+#define QK5_0                   32
+#define QR5_0                   2
+#define QK5_1                   32
+#define QR5_1                   2
+#define QK8_0                   32
+#define QR8_0                   1
+#define QK_K                    256
+#define K_QUANTS_PER_ITERATION  2
+
+typedef char int8_t;
+typedef uchar uint8_t;
+typedef short int16_t;
+typedef ushort uint16_t;
+typedef int int32_t;
+typedef uint uint32_t;
+
+//------------------------------------------------------------------------------
+// block_q4_0
+//------------------------------------------------------------------------------
+struct block_q4_0
+{
+    half d;
+    uint8_t qs[QK4_0 / 2];
+};
+
+//------------------------------------------------------------------------------
+// mul_vec_q_n_f32
+//------------------------------------------------------------------------------
+// function for calculate inner product between half a q4_0 block and 16 floats (yl), sumy is SUM(yl[i])
+// il indicates where the q4 quants begin (0 or QK4_0/4)
+// we assume that the yl's have been multiplied with the appropriate scale factor
+// that corresponds to the missing bit shifts (1, 1/16, 1/256, 1/4096)
+inline float block_q_4_0_dot_y(
+        global struct block_q4_0 * qb_curr,
+        float sumy,
+        private float * yl,
+        int il
+) {
+    float d = qb_curr->d;
+    float2 acc = 0.f;
+    global ushort * qs = ((global ushort *)qb_curr + 1 + il/2);
+    for (int i = 0; i < 8; i+=2) {
+        acc.s0 += yl[i + 0] * (qs[i / 2] & 0x000F)
+                + yl[i + 1] * (qs[i / 2] & 0x0F00);
+        acc.s1 += yl[i + 8] * (qs[i / 2] & 0x00F0)
+                + yl[i + 9] * (qs[i / 2] & 0xF000);
+    }
+    return d * (sumy * -8.f + acc.s0 + acc.s1);
+}
+
+#ifdef INTEL_GPU
+#define N_DST 4 // each SIMD group works on 4 rows
+#define N_SIMDGROUP 1 // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 16 // assuming SIMD group size is 16
+#elif defined (ADRENO_GPU)
+#define N_DST 4
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+
+inline void mul_vec_q_n_f32(
+        global void * src0,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+
+    const ulong nb = ne00/QK4_0;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    // (r0 * N_SIMDGROUP + get_sub_group_id()) is essenatially the linear global
+    // id of a SIMD group in the grid.
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset0 = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+
+    global struct block_q4_0 * x = (global struct block_q4_0 *) src0 + offset0;
+    global float             * y = (global float             *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float yl[16];       // src1 vector cache
+    float sumf[N_DST]={0.f};
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix * QK4_0 + il;
+
+    // each thread in a SIMD group deals with half a block.
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        float sumy = 0;
+        for (int i = 0; i < 8; i += 2) {
+            sumy += yb[i] + yb[i+1];
+            yl[i+0] = yb[i+ 0];
+            yl[i+1] = yb[i+ 1]/256.f;
+            sumy += yb[i+16] + yb[i+17];
+            yl[i+8] = yb[i+16]/16.f;
+            yl[i+9] = yb[i+17]/4096.f;
+        }
+
+        for (int row = 0; row < N_DST; row++) {
+            sumf[row] += block_q_4_0_dot_y(x+ib+row*nb, sumy, yl, il);
+        }
+
+        // One thread in a SIMD group (i.e., subgroup) handles a half block,
+        // hence then entire SIMD group handles SIMDWIDTH/2 blocks.
+        // y points to the activation matrix (of type float). Therefore for
+        // one thread, the # of blocks y should advance is SIMDWIDTH/2 (because
+        // SIMDWIDTH/2 blocks are processed by a SIMD group) - in terms of
+        // floats, it is QK4_0 * (SIMDWIDTH/2), where QK4_0 is the block size.
+        yb += QK4_0 * (N_SIMDWIDTH/2);
+    }
+
+    // The above does not work for Adreno - it produces incorrect results for
+    // row = 1, 2, 3 and only row = 0 gives the correct result.
+    // If N_DST is changed, the below array must be initialized accordingly.
+    // This also seems to perform better on Intel.
+    float tot[N_DST] = {
+        sub_group_reduce_add(sumf[0]), sub_group_reduce_add(sumf[1]),
+        sub_group_reduce_add(sumf[2]), sub_group_reduce_add(sumf[3])};
+    for (int row = 0; row < N_DST; ++row) {
+        if (get_sub_group_local_id() == 0 && first_row + row < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = tot[row];
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_q4_0_f32(
+        global void * src0,
+        ulong offset0,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    mul_vec_q_n_f32(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}

ggml/src/ggml-opencl/kernels/mul_mv_q4_0_f32_1d_16x_flat.cl

@@ -0,0 +1,307 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define QK4_0                   32
+#define QR4_0                   2
+#define QK4_1                   32
+#define QR4_1                   2
+#define QK5_0                   32
+#define QR5_0                   2
+#define QK5_1                   32
+#define QR5_1                   2
+#define QK8_0                   32
+#define QR8_0                   1
+#define QK_K                    256
+#define K_QUANTS_PER_ITERATION  2
+
+typedef char int8_t;
+typedef uchar uint8_t;
+typedef short int16_t;
+typedef ushort uint16_t;
+typedef int int32_t;
+typedef uint uint32_t;
+
+//------------------------------------------------------------------------------
+// block_q4_0
+//------------------------------------------------------------------------------
+struct block_q4_0
+{
+    half d;
+    uint8_t qs[QK4_0 / 2];
+};
+
+inline float mm_block_q_4_0_dot_y_flat(
+        global uchar * x,
+        global half  * dh,
+        float sumy,
+        float16 yl,
+        int il
+) {
+    float           d   = *dh;
+    global ushort * qs  = ((global ushort *)x + il/2);
+    float           acc = 0.f;
+
+    acc += yl.s0 * (qs[0] & 0x000F);
+    acc += yl.s1 * (qs[0] & 0x0F00);
+    acc += yl.s8 * (qs[0] & 0x00F0);
+    acc += yl.s9 * (qs[0] & 0xF000);
+
+    acc += yl.s2 * (qs[1] & 0x000F);
+    acc += yl.s3 * (qs[1] & 0x0F00);
+    acc += yl.sa * (qs[1] & 0x00F0);
+    acc += yl.sb * (qs[1] & 0xF000);
+
+    acc += yl.s4 * (qs[2] & 0x000F);
+    acc += yl.s5 * (qs[2] & 0x0F00);
+    acc += yl.sc * (qs[2] & 0x00F0);
+    acc += yl.sd * (qs[2] & 0xF000);
+
+    acc += yl.s6 * (qs[3] & 0x000F);
+    acc += yl.s7 * (qs[3] & 0x0F00);
+    acc += yl.se * (qs[3] & 0x00F0);
+    acc += yl.sf * (qs[3] & 0xF000);
+
+    return d * (sumy * -8.f + acc);
+}
+
+#ifdef INTEL_GPU
+#define N_DST 16 // each SIMD group works on 8 rows (in weights matrix)
+#define N_SIMDGROUP 1 // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 16 // assuming SIMD group size is 16
+#elif defined (ADRENO_GPU)
+#define N_DST 16
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+//
+// This variant performs 1d blocking with 16x output.
+// Eeach simdgroup outputs 16 values on `n0` dim (row in the output matrix).
+//
+inline void mul_mat_q_n_f32_1d_16x_flat(
+        global uchar * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    const int nb = ne00/QK4_0;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    // (r0 * N_SIMDGROUP + get_sub_group_id()) is the linear global id of
+    // a SIMD group in the grid. Each SIMD group produces N_DST values in the
+    // result, hence uses nb blocks, i.e., the offset becomes first_row*nb.
+    // Currently with llama2 7B, im is always 0.
+    // TODO: how to handle im/gqa*(nb*ne0)?
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    // The number of scales is the same as the number of blocks.
+    ulong offset0_d = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    // Each block contains QK4_0/2 uchars, hence offset for qs is as follows.
+    ulong offset0_q = (first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02)) * QK4_0/2;
+
+    global uchar * x = (global uchar *) src0_q + offset0_q;
+    global half  * d = (global half  *) src0_d + offset0_d;
+    global float * y = (global float *) src1   + r1*ne10 + im*ne00*ne1;
+
+    float16 yl;
+    float16 sumf = (float16)(0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
+                             0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f);
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix*QK4_0 + il;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        float sumy = 0.f;
+
+        sumy += yb[0];
+        sumy += yb[1];
+        sumy += yb[2];
+        sumy += yb[3];
+        sumy += yb[4];
+        sumy += yb[5];
+        sumy += yb[6];
+        sumy += yb[7];
+
+        sumy += yb[16];
+        sumy += yb[17];
+        sumy += yb[18];
+        sumy += yb[19];
+        sumy += yb[20];
+        sumy += yb[21];
+        sumy += yb[22];
+        sumy += yb[23];
+
+        yl.s0 = yb[0];
+        yl.s1 = yb[1]/256.f;
+
+        yl.s2 = yb[2];
+        yl.s3 = yb[3]/256.f;
+
+        yl.s4 = yb[4];
+        yl.s5 = yb[5]/256.f;
+
+        yl.s6 = yb[6];
+        yl.s7 = yb[7]/256.f;
+
+        yl.s8 = yb[16]/16.f;
+        yl.s9 = yb[17]/4096.f;
+
+        yl.sa = yb[18]/16.f;
+        yl.sb = yb[19]/4096.f;
+
+        yl.sc = yb[20]/16.f;
+        yl.sd = yb[21]/4096.f;
+
+        yl.se = yb[22]/16.f;
+        yl.sf = yb[23]/4096.f;
+
+        sumf.s0 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  0*nb*QK4_0/2, d + ib +  0*nb, sumy, yl, il);
+        sumf.s1 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  1*nb*QK4_0/2, d + ib +  1*nb, sumy, yl, il);
+        sumf.s2 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  2*nb*QK4_0/2, d + ib +  2*nb, sumy, yl, il);
+        sumf.s3 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  3*nb*QK4_0/2, d + ib +  3*nb, sumy, yl, il);
+
+        sumf.s4 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  4*nb*QK4_0/2, d + ib +  4*nb, sumy, yl, il);
+        sumf.s5 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  5*nb*QK4_0/2, d + ib +  5*nb, sumy, yl, il);
+        sumf.s6 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  6*nb*QK4_0/2, d + ib +  6*nb, sumy, yl, il);
+        sumf.s7 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  7*nb*QK4_0/2, d + ib +  7*nb, sumy, yl, il);
+
+        sumf.s8 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  8*nb*QK4_0/2, d + ib +  8*nb, sumy, yl, il);
+        sumf.s9 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 +  9*nb*QK4_0/2, d + ib +  9*nb, sumy, yl, il);
+        sumf.sa += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 10*nb*QK4_0/2, d + ib + 10*nb, sumy, yl, il);
+        sumf.sb += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 11*nb*QK4_0/2, d + ib + 11*nb, sumy, yl, il);
+
+        sumf.sc += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 12*nb*QK4_0/2, d + ib + 12*nb, sumy, yl, il);
+        sumf.sd += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 13*nb*QK4_0/2, d + ib + 13*nb, sumy, yl, il);
+        sumf.se += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 14*nb*QK4_0/2, d + ib + 14*nb, sumy, yl, il);
+        sumf.sf += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 15*nb*QK4_0/2, d + ib + 15*nb, sumy, yl, il);
+
+        yb += QK4_0 * (N_SIMDWIDTH/2);
+    }
+
+    float16 tot = (float16)(
+        sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
+        sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3),
+        sub_group_reduce_add(sumf.s4), sub_group_reduce_add(sumf.s5),
+        sub_group_reduce_add(sumf.s6), sub_group_reduce_add(sumf.s7),
+
+        sub_group_reduce_add(sumf.s8), sub_group_reduce_add(sumf.s9),
+        sub_group_reduce_add(sumf.sa), sub_group_reduce_add(sumf.sb),
+        sub_group_reduce_add(sumf.sc), sub_group_reduce_add(sumf.sd),
+        sub_group_reduce_add(sumf.se), sub_group_reduce_add(sumf.sf)
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
+        }
+        if (first_row + 1 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
+        }
+        if (first_row + 2 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
+        }
+        if (first_row + 3 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
+        }
+
+        if (first_row + 4 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 4] = tot.s4;
+        }
+        if (first_row + 5 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 5] = tot.s5;
+        }
+        if (first_row + 6 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 6] = tot.s6;
+        }
+        if (first_row + 7 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 7] = tot.s7;
+        }
+
+        if (first_row + 8 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 8] = tot.s8;
+        }
+        if (first_row + 9 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 9] = tot.s9;
+        }
+        if (first_row + 10 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 10] = tot.sa;
+        }
+        if (first_row + 11 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 11] = tot.sb;
+        }
+
+        if (first_row + 12 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 12] = tot.sc;
+        }
+        if (first_row + 13 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 13] = tot.sd;
+        }
+        if (first_row + 14 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 14] = tot.se;
+        }
+        if (first_row + 15 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 15] = tot.sf;
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_q4_0_f32_1d_16x_flat(
+        global uchar * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    mul_mat_q_n_f32_1d_16x_flat(src0_q, src0_d, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}

ggml/src/ggml-opencl/kernels/mul_mv_q4_0_f32_1d_8x_flat.cl

@@ -0,0 +1,265 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define QK4_0                   32
+#define QR4_0                   2
+#define QK4_1                   32
+#define QR4_1                   2
+#define QK5_0                   32
+#define QR5_0                   2
+#define QK5_1                   32
+#define QR5_1                   2
+#define QK8_0                   32
+#define QR8_0                   1
+#define QK_K                    256
+#define K_QUANTS_PER_ITERATION  2
+
+typedef char int8_t;
+typedef uchar uint8_t;
+typedef short int16_t;
+typedef ushort uint16_t;
+typedef int int32_t;
+typedef uint uint32_t;
+
+//------------------------------------------------------------------------------
+// block_q4_0
+//------------------------------------------------------------------------------
+struct block_q4_0
+{
+    half d;
+    uint8_t qs[QK4_0 / 2];
+};
+
+inline float mm_block_q_4_0_dot_y_flat(
+        global uchar * x,
+        global half  * dh,
+        float sumy,
+        float16 yl,
+        int il
+) {
+    float           d   = *dh;
+    global ushort * qs  = ((global ushort *)x + il/2);
+    float           acc = 0.f;
+
+    acc += yl.s0 * (qs[0] & 0x000F);
+    acc += yl.s1 * (qs[0] & 0x0F00);
+    acc += yl.s8 * (qs[0] & 0x00F0);
+    acc += yl.s9 * (qs[0] & 0xF000);
+
+    acc += yl.s2 * (qs[1] & 0x000F);
+    acc += yl.s3 * (qs[1] & 0x0F00);
+    acc += yl.sa * (qs[1] & 0x00F0);
+    acc += yl.sb * (qs[1] & 0xF000);
+
+    acc += yl.s4 * (qs[2] & 0x000F);
+    acc += yl.s5 * (qs[2] & 0x0F00);
+    acc += yl.sc * (qs[2] & 0x00F0);
+    acc += yl.sd * (qs[2] & 0xF000);
+
+    acc += yl.s6 * (qs[3] & 0x000F);
+    acc += yl.s7 * (qs[3] & 0x0F00);
+    acc += yl.se * (qs[3] & 0x00F0);
+    acc += yl.sf * (qs[3] & 0xF000);
+
+    return d * (sumy * -8.f + acc);
+}
+
+#ifdef INTEL_GPU
+#define N_DST 8 // each SIMD group works on 8 rows (in weights matrix)
+#define N_SIMDGROUP 1 // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 16 // assuming SIMD group size is 16
+#elif defined (ADRENO_GPU)
+#define N_DST 8
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+//
+// This variant performs 1d blocking with 8x output.
+// Eeach simdgroup outputs 8 values on `n0` dim (row in the output matrix).
+//
+inline void mul_mat_q_n_f32_1d_8x_flat(
+        global uchar * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    const int nb = ne00/QK4_0;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    // (r0 * N_SIMDGROUP + get_sub_group_id()) is the linear global id of
+    // a SIMD group in the grid. Each SIMD group produces N_DST values in the
+    // result, hence uses nb blocks, i.e., the offset becomes first_row*nb.
+    // Currently with llama2 7B, im is always 0.
+    // TODO: how to handle im/gqa*(nb*ne0)?
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    // The number of scales is the same as the number of blocks.
+    ulong offset0_d = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    // Each block contains QK4_0/2 uchars, hence offset for qs is as follows.
+    ulong offset0_q = (first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02)) * QK4_0/2;
+
+    global uchar * x = (global uchar *) src0_q + offset0_q;
+    global half  * d = (global half  *) src0_d + offset0_d;
+    global float * y = (global float *) src1   + r1*ne10 + im*ne00*ne1;
+
+    float16 yl;
+    float8 sumf = (float8)(0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f);
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix*QK4_0 + il;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        float sumy = 0.f;
+
+        sumy += yb[0];
+        sumy += yb[1];
+        sumy += yb[2];
+        sumy += yb[3];
+        sumy += yb[4];
+        sumy += yb[5];
+        sumy += yb[6];
+        sumy += yb[7];
+
+        sumy += yb[16];
+        sumy += yb[17];
+        sumy += yb[18];
+        sumy += yb[19];
+        sumy += yb[20];
+        sumy += yb[21];
+        sumy += yb[22];
+        sumy += yb[23];
+
+        yl.s0 = yb[0];
+        yl.s1 = yb[1]/256.f;
+
+        yl.s2 = yb[2];
+        yl.s3 = yb[3]/256.f;
+
+        yl.s4 = yb[4];
+        yl.s5 = yb[5]/256.f;
+
+        yl.s6 = yb[6];
+        yl.s7 = yb[7]/256.f;
+
+        yl.s8 = yb[16]/16.f;
+        yl.s9 = yb[17]/4096.f;
+
+        yl.sa = yb[18]/16.f;
+        yl.sb = yb[19]/4096.f;
+
+        yl.sc = yb[20]/16.f;
+        yl.sd = yb[21]/4096.f;
+
+        yl.se = yb[22]/16.f;
+        yl.sf = yb[23]/4096.f;
+
+        sumf.s0 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 0*nb*QK4_0/2, d + ib + 0*nb, sumy, yl, il);
+        sumf.s1 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 1*nb*QK4_0/2, d + ib + 1*nb, sumy, yl, il);
+        sumf.s2 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 2*nb*QK4_0/2, d + ib + 2*nb, sumy, yl, il);
+        sumf.s3 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 3*nb*QK4_0/2, d + ib + 3*nb, sumy, yl, il);
+
+        sumf.s4 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 4*nb*QK4_0/2, d + ib + 4*nb, sumy, yl, il);
+        sumf.s5 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 5*nb*QK4_0/2, d + ib + 5*nb, sumy, yl, il);
+        sumf.s6 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 6*nb*QK4_0/2, d + ib + 6*nb, sumy, yl, il);
+        sumf.s7 += mm_block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 7*nb*QK4_0/2, d + ib + 7*nb, sumy, yl, il);
+
+        yb += QK4_0 * (N_SIMDWIDTH/2);
+    }
+
+    float8 tot = (float8)(
+        sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
+        sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3),
+        sub_group_reduce_add(sumf.s4), sub_group_reduce_add(sumf.s5),
+        sub_group_reduce_add(sumf.s6), sub_group_reduce_add(sumf.s7)
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
+        }
+        if (first_row + 1 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
+        }
+        if (first_row + 2 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
+        }
+        if (first_row + 3 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
+        }
+
+        if (first_row + 4 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 4] = tot.s4;
+        }
+        if (first_row + 5 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 5] = tot.s5;
+        }
+        if (first_row + 6 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 6] = tot.s6;
+        }
+        if (first_row + 7 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 7] = tot.s7;
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_q4_0_f32_1d_8x_flat(
+        global uchar * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    mul_mat_q_n_f32_1d_8x_flat(src0_q, src0_d, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}

ggml/src/ggml-opencl/kernels/mul_mv_q4_0_f32_8x_flat.cl

@@ -0,0 +1,272 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define QK4_0                   32
+#define QR4_0                   2
+#define QK4_1                   32
+#define QR4_1                   2
+#define QK5_0                   32
+#define QR5_0                   2
+#define QK5_1                   32
+#define QR5_1                   2
+#define QK8_0                   32
+#define QR8_0                   1
+#define QK_K                    256
+#define K_QUANTS_PER_ITERATION  2
+
+typedef char int8_t;
+typedef uchar uint8_t;
+typedef short int16_t;
+typedef ushort uint16_t;
+typedef int int32_t;
+typedef uint uint32_t;
+
+//------------------------------------------------------------------------------
+// block_q4_0
+//------------------------------------------------------------------------------
+struct block_q4_0
+{
+    half d;
+    uint8_t qs[QK4_0 / 2];
+};
+
+// This function requires the original shuffled weights.
+// As a reminder, the original weights are shuffled so that (q[0], q[16]) are
+// packed together in a byte, so are (q[1], q[17]) and so on.
+inline float block_q_4_0_dot_y_flat(
+        global uchar * x,
+        global half  * dh,
+        float sumy,
+        float16 yl,
+        int il
+) {
+    float           d   = *dh;
+    global ushort * qs  = ((global ushort *)x + il/2);
+    float           acc = 0.f;
+
+    acc += yl.s0 * (qs[0] & 0x000F);
+    acc += yl.s1 * (qs[0] & 0x0F00);
+    acc += yl.s8 * (qs[0] & 0x00F0);
+    acc += yl.s9 * (qs[0] & 0xF000);
+
+    acc += yl.s2 * (qs[1] & 0x000F);
+    acc += yl.s3 * (qs[1] & 0x0F00);
+    acc += yl.sa * (qs[1] & 0x00F0);
+    acc += yl.sb * (qs[1] & 0xF000);
+
+    acc += yl.s4 * (qs[2] & 0x000F);
+    acc += yl.s5 * (qs[2] & 0x0F00);
+    acc += yl.sc * (qs[2] & 0x00F0);
+    acc += yl.sd * (qs[2] & 0xF000);
+
+    acc += yl.s6 * (qs[3] & 0x000F);
+    acc += yl.s7 * (qs[3] & 0x0F00);
+    acc += yl.se * (qs[3] & 0x00F0);
+    acc += yl.sf * (qs[3] & 0xF000);
+
+    return d * (sumy * -8.f + acc);
+}
+
+//
+// This variant outputs 8 values.
+//
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define N_DST 8 // each SIMD group works on 8 rows
+#define N_SIMDGROUP 1 // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 16 // assuming SIMD group size is 32
+#elif defined (ADRENO_GPU)
+#define N_DST 8
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+
+inline void mul_vec_q_n_f32_8x_flat(
+        global uchar * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    const ulong nb = ne00/QK4_0;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    // (r0 * N_SIMDGROUP + get_sub_group_id()) is the linear global id of
+    // a SIMD group in the grid. Each SIMD group produces N_DST values in the
+    // result, hence uses nb blocks, i.e., the offset becomes first_row*nb.
+    // Currently with llama2 7B, im is always 0.
+    // TODO: how to handle im/gqa*(nb*ne0)?
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    // The number of scales is the same as the number of blocks.
+    ulong offset0_d = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+    // Each block contains QK4_0/2 uchars, hence offset for qs is as follows.
+    ulong offset0_q = (first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02)) * QK4_0/2;
+
+    global uchar * x = (global uchar *) src0_q + offset0_q;
+    global half  * d = (global half  *) src0_d + offset0_d;
+    global float * y = (global float *) src1   + r1*ne10 + im*ne00*ne1;
+
+    float16 yl;
+    float8 sumf = 0.f;
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix*QK4_0 + il;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        float sumy = 0.f;
+
+        sumy += yb[0];
+        sumy += yb[1];
+        sumy += yb[2];
+        sumy += yb[3];
+        sumy += yb[4];
+        sumy += yb[5];
+        sumy += yb[6];
+        sumy += yb[7];
+
+        sumy += yb[16];
+        sumy += yb[17];
+        sumy += yb[18];
+        sumy += yb[19];
+        sumy += yb[20];
+        sumy += yb[21];
+        sumy += yb[22];
+        sumy += yb[23];
+
+        yl.s0 = yb[0];
+        yl.s1 = yb[1]/256.f;
+
+        yl.s2 = yb[2];
+        yl.s3 = yb[3]/256.f;
+
+        yl.s4 = yb[4];
+        yl.s5 = yb[5]/256.f;
+
+        yl.s6 = yb[6];
+        yl.s7 = yb[7]/256.f;
+
+        yl.s8 = yb[16]/16.f;
+        yl.s9 = yb[17]/4096.f;
+
+        yl.sa = yb[18]/16.f;
+        yl.sb = yb[19]/4096.f;
+
+        yl.sc = yb[20]/16.f;
+        yl.sd = yb[21]/4096.f;
+
+        yl.se = yb[22]/16.f;
+        yl.sf = yb[23]/4096.f;
+
+        sumf.s0 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 0*nb*QK4_0/2, d + ib + 0*nb, sumy, yl, il);
+        sumf.s1 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 1*nb*QK4_0/2, d + ib + 1*nb, sumy, yl, il);
+        sumf.s2 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 2*nb*QK4_0/2, d + ib + 2*nb, sumy, yl, il);
+        sumf.s3 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 3*nb*QK4_0/2, d + ib + 3*nb, sumy, yl, il);
+
+        sumf.s4 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 4*nb*QK4_0/2, d + ib + 4*nb, sumy, yl, il);
+        sumf.s5 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 5*nb*QK4_0/2, d + ib + 5*nb, sumy, yl, il);
+        sumf.s6 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 6*nb*QK4_0/2, d + ib + 6*nb, sumy, yl, il);
+        sumf.s7 += block_q_4_0_dot_y_flat(x + ib*QK4_0/2 + 7*nb*QK4_0/2, d + ib + 7*nb, sumy, yl, il);
+
+        yb += QK4_0 * (N_SIMDWIDTH/2);
+    }
+
+    float8 tot = (float8)(
+        sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
+        sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3),
+        sub_group_reduce_add(sumf.s4), sub_group_reduce_add(sumf.s5),
+        sub_group_reduce_add(sumf.s6), sub_group_reduce_add(sumf.s7)
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
+        }
+        if (first_row + 1 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
+        }
+        if (first_row + 2 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
+        }
+        if (first_row + 3 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
+        }
+
+        if (first_row + 4 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 4] = tot.s4;
+        }
+        if (first_row + 5 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 5] = tot.s5;
+        }
+        if (first_row + 6 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 6] = tot.s6;
+        }
+        if (first_row + 7 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 7] = tot.s7;
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_q4_0_f32_8x_flat(
+        global uchar * src0_q,
+        global half  * src0_d,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    mul_vec_q_n_f32_8x_flat(src0_q, src0_d, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}

ggml/src/ggml-opencl/kernels/mul_mv_q4_0_f32_v.cl

@@ -0,0 +1,254 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define QK4_0                   32
+#define QR4_0                   2
+#define QK4_1                   32
+#define QR4_1                   2
+#define QK5_0                   32
+#define QR5_0                   2
+#define QK5_1                   32
+#define QR5_1                   2
+#define QK8_0                   32
+#define QR8_0                   1
+#define QK_K                    256
+#define K_QUANTS_PER_ITERATION  2
+
+typedef char int8_t;
+typedef uchar uint8_t;
+typedef short int16_t;
+typedef ushort uint16_t;
+typedef int int32_t;
+typedef uint uint32_t;
+
+//------------------------------------------------------------------------------
+// block_q4_0
+//------------------------------------------------------------------------------
+struct block_q4_0
+{
+    half d;
+    uint8_t qs[QK4_0 / 2];
+};
+
+//
+// This variant unrolls the loops and uses vector types instead of pointers.
+// It improves performance on Adreno but not so much on Intel.
+//
+inline float block_q_4_0_dot_y_v(
+        global struct block_q4_0 * qb_curr,
+        float sumy,
+        float16 yl,
+        int il
+) {
+    float d = qb_curr->d;
+    float acc = 0.f;
+    global ushort * qs = ((global ushort *)qb_curr + 1 + il/2);
+
+    acc += yl.s0 * (qs[0] & 0x000F);
+    acc += yl.s1 * (qs[0] & 0x0F00);
+    acc += yl.s8 * (qs[0] & 0x00F0);
+    acc += yl.s9 * (qs[0] & 0xF000);
+
+    acc += yl.s2 * (qs[1] & 0x000F);
+    acc += yl.s3 * (qs[1] & 0x0F00);
+    acc += yl.sa * (qs[1] & 0x00F0);
+    acc += yl.sb * (qs[1] & 0xF000);
+
+    acc += yl.s4 * (qs[2] & 0x000F);
+    acc += yl.s5 * (qs[2] & 0x0F00);
+    acc += yl.sc * (qs[2] & 0x00F0);
+    acc += yl.sd * (qs[2] & 0xF000);
+
+    acc += yl.s6 * (qs[3] & 0x000F);
+    acc += yl.s7 * (qs[3] & 0x0F00);
+    acc += yl.se * (qs[3] & 0x00F0);
+    acc += yl.sf * (qs[3] & 0xF000);
+
+    return d * (sumy * -8.f + acc);
+}
+
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define N_DST 4 // each SIMD group works on 4 rows
+#define N_SIMDGROUP 1 // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 16 // assuming SIMD group size is 16
+#elif defined (ADRENO_GPU)
+#define N_DST 4
+#define N_SIMDGROUP 1
+#define N_SIMDWIDTH 64
+#endif
+
+inline void mul_vec_q_n_f32_v(
+        global void * src0,
+        global float * src1,
+        global float * dst,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    const ulong nb = ne00/QK4_0;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    // (r0 * N_SIMDGROUP + get_sub_group_id()) is essenatially the linear global
+    // id of a SIMD group in the grid.
+    int first_row = (r0 * N_SIMDGROUP + get_sub_group_id()) * N_DST;
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset0 = first_row * nb + (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+
+    global struct block_q4_0 * x = (global struct block_q4_0 *) src0 + offset0;
+    global float             * y = (global float             *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float16 yl;       // src1 vector cache
+    float4 sumf = (float4)(0.f, 0.f, 0.f, 0.f);
+
+    int ix = get_sub_group_local_id()/2;
+    int il = 8*(get_sub_group_local_id()%2);
+
+    global float * yb = y + ix * QK4_0 + il;
+
+    // each thread in a SIMD group deals with half a block.
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        float sumy = 0;
+
+        sumy += yb[0];
+        sumy += yb[1];
+        sumy += yb[2];
+        sumy += yb[3];
+        sumy += yb[4];
+        sumy += yb[5];
+        sumy += yb[6];
+        sumy += yb[7];
+
+        sumy += yb[16];
+        sumy += yb[17];
+        sumy += yb[18];
+        sumy += yb[19];
+        sumy += yb[20];
+        sumy += yb[21];
+        sumy += yb[22];
+        sumy += yb[23];
+
+
+        yl.s0 = yb[0];
+        yl.s1 = yb[1]/256.f;
+
+        yl.s2 = yb[2];
+        yl.s3 = yb[3]/256.f;
+
+        yl.s4 = yb[4];
+        yl.s5 = yb[5]/256.f;
+
+        yl.s6 = yb[6];
+        yl.s7 = yb[7]/256.f;
+
+        yl.s8 = yb[16]/16.f;
+        yl.s9 = yb[17]/4096.f;
+
+        yl.sa = yb[18]/16.f;
+        yl.sb = yb[19]/4096.f;
+
+        yl.sc = yb[20]/16.f;
+        yl.sd = yb[21]/4096.f;
+
+        yl.se = yb[22]/16.f;
+        yl.sf = yb[23]/4096.f;
+
+        sumf.s0 += block_q_4_0_dot_y_v(x+ib+0*nb, sumy, yl, il);
+        sumf.s1 += block_q_4_0_dot_y_v(x+ib+1*nb, sumy, yl, il);
+        sumf.s2 += block_q_4_0_dot_y_v(x+ib+2*nb, sumy, yl, il);
+        sumf.s3 += block_q_4_0_dot_y_v(x+ib+3*nb, sumy, yl, il);
+
+        // One thread in a SIMD group (i.e., subgroup) handles a half block,
+        // hence then entire SIMD group handles SIMDWIDTH/2 blocks.
+        // y points to the activation matrix (of type float). Therefore for
+        // one thread, the # of blocks y should advance is SIMDWIDTH/2 (because
+        // SIMDWIDTH/2 blocks are processed by a SIMD group) - in terms of
+        // floats, it is QK4_0 * (SIMDWIDTH/2), where QK4_0 is the block size.
+        yb += QK4_0 * (N_SIMDWIDTH/2);
+    }
+
+    // The above does not work for Adreno - it produces incorrect results for
+    // row = 1, 2, 3 and only row = 0 gives the correct result.
+    // If N_DST is changed, the below array must be initialized accordingly.
+    // This also seems to perform better on Intel.
+    float4 tot = (float4)(
+        sub_group_reduce_add(sumf.s0), sub_group_reduce_add(sumf.s1),
+        sub_group_reduce_add(sumf.s2), sub_group_reduce_add(sumf.s3)
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 0] = tot.s0;
+        }
+        if (first_row + 1 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 1] = tot.s1;
+        }
+        if (first_row + 2 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 2] = tot.s2;
+        }
+        if (first_row + 3 < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + 3] = tot.s3;
+        }
+    }
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_q4_0_f32_v(
+        global void * src0,
+        ulong offset0,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    mul_vec_q_n_f32_v(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3);
+}

ggml/src/ggml-opencl/kernels/mul_mv_q6_k.cl

@@ -0,0 +1,190 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define QK4_0                   32
+#define QR4_0                   2
+#define QK4_1                   32
+#define QR4_1                   2
+#define QK5_0                   32
+#define QR5_0                   2
+#define QK5_1                   32
+#define QR5_1                   2
+#define QK8_0                   32
+#define QR8_0                   1
+#define QK_K                    256
+#define K_QUANTS_PER_ITERATION  2
+
+typedef char int8_t;
+typedef uchar uint8_t;
+typedef short int16_t;
+typedef ushort uint16_t;
+typedef int int32_t;
+typedef uint uint32_t;
+
+//------------------------------------------------------------------------------
+// block_q6_K
+//------------------------------------------------------------------------------
+// 6-bit quantization
+// weight is represented as x = a * q
+// 16 blocks of 16 elements each
+// Effectively 6.5625 bits per weight
+typedef struct {
+    uint8_t ql[QK_K/2];      // quants, lower 4 bits
+    uint8_t qh[QK_K/4];      // quants, upper 2 bits
+    int8_t  scales[QK_K/16]; // scales, quantized with 8 bits
+    half d;             // super-block scale
+} block_q6_K;
+
+//------------------------------------------------------------------------------
+// kernel_mul_mv_q6_K_f32
+//------------------------------------------------------------------------------
+
+#undef N_DST
+#undef N_SIMDGROUP
+#undef N_SIMDWIDTH
+
+#ifdef INTEL_GPU
+#define N_DST 1 // number of rows each SIMD group works on
+#define N_SIMDGROUP 2 // number of SIMD groups in a thread group
+#define N_SIMDWIDTH 16 // SIMD group size
+#elif defined (ADRENO_GPU)
+#define N_DST 1
+#define N_SIMDGROUP 2
+#define N_SIMDWIDTH 64
+#endif
+
+#define BLOCK_STRIDE (N_SIMDWIDTH/16) // number of blocks each subgroup processes
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mv_q6_K_f32(
+        global void * src0,
+        ulong offset0,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    uchar kmask1 = 0x03;
+    uchar kmask2 = 0x0C;
+    uchar kmask3 = 0x30;
+    uchar kmask4 = 0xC0;
+
+    int nb = ne00/QK_K;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    int row = N_SIMDGROUP * r0 + get_sub_group_id();
+
+    int i12 = im%ne12;
+    int i13 = im/ne12;
+
+    ulong offset_src0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02);
+
+    global block_q6_K * x = (global block_q6_K *) src0 + row*nb + offset_src0;
+    global float      * yy = (global float     *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float sumf = 0;
+
+    // For Q6_K quantization, 16 values forms a subblock, 16 subblock forms a
+    // block. Values in a subblock shares a scale that is quantized with 8 bits;
+    // the entire block shares a single floating point scale.
+    // For work distribution, each thread processes a subblock (16 weights), hence
+    // 16 threads process a (super) block -- a subgroup thus handles SIMDWIDTH/16
+    // (super) blocks -- this is the block stride.
+    // The 16 threads that process a (super) block are split into 2 portions, each has
+    // 8 threads; each portion works on 8 subblocks.
+    // For subgroup of 16 threads, the entire subgroup works on a single (super) block
+    // before moving to the next (super) block. Thread0 - thread7 work on the
+    // first 8 subblocks; thread8 - thread15 works on the last 8 subblocks.
+    // Thread0 - thread3 work on subblocks 0, 2, 4, 6; thread4 - thread7 work on
+    // subblocks 1, 3, 5, 7. Each thread does not work on an entire subblock, but
+    // works on a total of 16 weight values.
+    int tid  = get_sub_group_local_id()/BLOCK_STRIDE; // first block_stride groups have tid=0
+    int ix   = get_sub_group_local_id()%BLOCK_STRIDE; // first block is 0..block_stride-1
+    int ip   = tid/8;   // first or second half of (super) block (0 or 1)
+    int il   = tid%8;   // each half has 8 parts, one per scale
+    int n    = 4;       // 4 scales at a time (and 4 sums)
+    int l0   = n*il;    // offset into half-block, 0..28
+    int is   = 8*ip + l0/16; // 0, 1, 8, 9
+
+    int y_offset = 128*ip + l0;
+    int q_offset_l = 64*ip + l0;
+    int q_offset_h = 32*ip + l0;
+
+    for (int i = ix; i < nb; i += BLOCK_STRIDE) {
+
+        global uint8_t * q1 = x[i].ql + q_offset_l;
+        global uint8_t * q2 = q1 + QK_K/8;
+        global uint8_t * qh = x[i].qh + q_offset_h;
+        global int8_t  * sc = x[i].scales + is;
+
+        global float * y = yy + i * QK_K + y_offset;
+
+        float dall = x[i].d;
+
+        float4 sums = {0.f, 0.f, 0.f, 0.f};
+
+        sums.s0 += y[0+ 0] * ((float)((q1[0] & 0xF) | ((qh[0] & kmask1) << 4)) - 32.f);
+        sums.s1 += y[0+32] * ((float)((q2[0] & 0xF) | ((qh[0] & kmask2) << 2)) - 32.f);
+        sums.s2 += y[0+64] * ((float)((q1[0]  >> 4) | ((qh[0] & kmask3) << 0)) - 32.f);
+        sums.s3 += y[0+96] * ((float)((q2[0]  >> 4) | ((qh[0] & kmask4) >> 2)) - 32.f);
+
+        sums.s0 += y[1+ 0] * ((float)((q1[1] & 0xF) | ((qh[1] & kmask1) << 4)) - 32.f);
+        sums.s1 += y[1+32] * ((float)((q2[1] & 0xF) | ((qh[1] & kmask2) << 2)) - 32.f);
+        sums.s2 += y[1+64] * ((float)((q1[1]  >> 4) | ((qh[1] & kmask3) << 0)) - 32.f);
+        sums.s3 += y[1+96] * ((float)((q2[1]  >> 4) | ((qh[1] & kmask4) >> 2)) - 32.f);
+
+        sums.s0 += y[2+ 0] * ((float)((q1[2] & 0xF) | ((qh[2] & kmask1) << 4)) - 32.f);
+        sums.s1 += y[2+32] * ((float)((q2[2] & 0xF) | ((qh[2] & kmask2) << 2)) - 32.f);
+        sums.s2 += y[2+64] * ((float)((q1[2]  >> 4) | ((qh[2] & kmask3) << 0)) - 32.f);
+        sums.s3 += y[2+96] * ((float)((q2[2]  >> 4) | ((qh[2] & kmask4) >> 2)) - 32.f);
+
+        sums.s0 += y[3+ 0] * ((float)((q1[3] & 0xF) | ((qh[3] & kmask1) << 4)) - 32.f);
+        sums.s1 += y[3+32] * ((float)((q2[3] & 0xF) | ((qh[3] & kmask2) << 2)) - 32.f);
+        sums.s2 += y[3+64] * ((float)((q1[3]  >> 4) | ((qh[3] & kmask3) << 0)) - 32.f);
+        sums.s3 += y[3+96] * ((float)((q2[3]  >> 4) | ((qh[3] & kmask4) >> 2)) - 32.f);
+
+        sumf += dall * (sums.s0 * sc[0] + sums.s1 * sc[2] + sums.s2 * sc[4] + sums.s3 * sc[6]);
+    }
+
+    float tot = sub_group_reduce_add(sumf);
+    if (get_sub_group_local_id() == 0) {
+        dst[r1*ne0 + im*ne0*ne1 + row] = tot;
+    }
+}

ggml/src/ggml-opencl/kernels/norm.cl

@@ -0,0 +1,81 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+//------------------------------------------------------------------------------
+// norm
+//------------------------------------------------------------------------------
+kernel void kernel_norm(
+        global void * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        float eps,
+        local float * sum
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    dst = (global void*)((global char*)dst + offsetd);
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    global float * x = (global float *) ((global char *) src0 + i03*nb03 + i02*nb02 + i01*nb01);
+
+    // MEAN
+    // parallel sum
+    sum[get_local_id(0)] = 0.0f;
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        sum[get_local_id(0)] += x[i00];
+    }
+    // reduce
+    barrier(CLK_LOCAL_MEM_FENCE);
+    for (uint i = get_local_size(0)/2; i > 0; i /= 2) {
+        if (get_local_id(0) < i) {
+            sum[get_local_id(0)] += sum[get_local_id(0) + i];
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    float mean  = sum[0] / ne00;
+
+    // recenter and VARIANCE
+    barrier(CLK_LOCAL_MEM_FENCE);
+    global float * y = dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+    sum[get_local_id(0)] = 0.0f;
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        y[i00] = x[i00] - mean;
+        sum[get_local_id(0)] += y[i00] * y[i00];
+    }
+
+    // reduce
+    barrier(CLK_LOCAL_MEM_FENCE);
+    for (uint i = get_local_size(0)/2; i > 0; i /= 2) {
+        if (get_local_id(0) < i) {
+            sum[get_local_id(0)] += sum[get_local_id(0) + i];
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    float variance = sum[0] / ne00;
+
+    float scale = 1.0f/sqrt(variance + eps);
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        y[i00] = y[i00] * scale;
+    }
+}

ggml/src/ggml-opencl/kernels/relu.cl

@@ -0,0 +1,16 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+//------------------------------------------------------------------------------
+// relu
+//------------------------------------------------------------------------------
+kernel void kernel_relu(
+        global float * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    dst[get_global_id(0)] = fmax(0.0f, src0[get_global_id(0)]);
+}

ggml/src/ggml-opencl/kernels/rms_norm.cl

@@ -0,0 +1,96 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+//------------------------------------------------------------------------------
+// rms_norm
+//------------------------------------------------------------------------------
+// This kernel depends on subgroup size.
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_32
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_rms_norm(
+        global void * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        float eps,
+        local float * sum // Note, the size depends on number of subgroups
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    global float4 * x = (global float4 *) ((global char *) src0 + i03*nb03 + i02*nb02 + i01*nb01);
+    global float * x_scalar = (global float *) x;
+    float4 sumf = 0;
+    float all_sum = 0;
+
+    // parallel sum
+    for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
+        sumf += x[i00] * x[i00];
+    }
+    all_sum = sumf.s0 + sumf.s1 + sumf.s2 + sumf.s3;
+    all_sum = sub_group_reduce_add(all_sum);
+    if (get_sub_group_local_id() == 0) {
+        sum[get_sub_group_id()] = all_sum;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+    // broadcast
+    for (uint i = get_local_size(0) / get_max_sub_group_size() / 2; i > 0; i /= 2) {
+       if (get_local_id(0) < i) {
+           sum[get_local_id(0)] += sum[get_local_id(0) + i];
+       }
+    }
+    if (get_local_id(0) == 0) {
+        for (int i = 4 * (ne00 / 4); i < ne00; i++) {
+            sum[0] += x_scalar[i];
+        }
+        sum[0] /= ne00;
+    }
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    const float mean  = sum[0];
+    const float scale = 1.0f/sqrt(mean + eps);
+
+    global float4 * y = (global float4 *) (dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
+    global float * y_scalar = (global float *) y;
+    for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
+        y[i00] = x[i00] * scale;
+    }
+    if (get_local_id(0) == 0) {
+        for (int i00 = 4 * (ne00 / 4); i00 < ne00; i00++) {
+            y_scalar[i00] = x_scalar[i00] * scale;
+        }
+    }
+}

ggml/src/ggml-opencl/kernels/rope.cl

@@ -0,0 +1,721 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+//------------------------------------------------------------------------------
+// kernel_rope
+//------------------------------------------------------------------------------
+float rope_yarn_ramp(float low, float high, int i0) {
+    const float y = (i0 / 2 - low) / max(0.001f, high - low);
+    return 1.0f - min(1.0f, max(0.0f, y));
+}
+
+// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
+// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
+float2 rope_yarn(
+    float theta_extrap, float freq_scale, float2 corr_dims, int i0, float ext_factor, float mscale
+) {
+    // Get n-d rotational scaling corrected for extrapolation
+    float theta_interp = freq_scale * theta_extrap;
+    float theta = theta_interp;
+    if (ext_factor != 0.0f) {
+        float ramp_mix = rope_yarn_ramp(corr_dims.s0, corr_dims.s1, i0) * ext_factor;
+        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+
+        // Get n-d magnitude scaling corrected for interpolation
+        mscale *= 1.0f + 0.1f * log(1.0f / freq_scale);
+    }
+    return (float2)(cos(theta) * mscale, sin(theta) * mscale);
+}
+
+// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get
+// `corr_fac(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))`
+float rope_yarn_corr_factor(int n_dims, int n_ctx_orig, float n_rot, float base) {
+    return n_dims * log(n_ctx_orig / (n_rot * 2 * M_PI_F)) / (2 * log(base));
+}
+
+float2 rope_yarn_corr_dims(
+    int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow
+) {
+    // start and end correction dims
+    return (float2)(
+        max(0.0f,         floor(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_fast, freq_base))),
+        min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_slow, freq_base)))
+    );
+}
+
+kernel void kernel_rope_norm_f32(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * src2,
+        ulong offset2,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3,
+        int n_past,
+        int n_dims,
+        int n_ctx_orig,
+        float freq_base,
+        float freq_scale,
+        float ext_factor,
+        float attn_factor,
+        float beta_fast,
+        float beta_slow
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    src2 = (global float*)((global char*)src2 + offset2);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i3 = get_group_id(2);
+    int i2 = get_group_id(1);
+    int i1 = get_group_id(0);
+
+    float2 corr_dims = rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow);
+
+    global int * pos = src1;
+
+    float theta_base = (float) pos[i2];
+    float inv_ndims = -1.f/n_dims;
+
+    for (int i0 = 2*get_local_id(0); i0 < ne0; i0 += 2*get_local_size(0)) {
+        if (i0 < n_dims) {
+            int ic = i0/2;
+
+            float theta = theta_base * pow(freq_base, inv_ndims*i0);
+
+            float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
+
+            float2 cos_sin_theta = rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor);
+
+            global float * src       = (global float *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+            global float * dst_data  = (global float *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+            float x0 = src[0];
+            float x1 = src[1];
+
+            dst_data[0] = x0*cos_sin_theta.s0 - x1*cos_sin_theta.s1;
+            dst_data[1] = x0*cos_sin_theta.s1 + x1*cos_sin_theta.s0;
+        } else {
+            global float * src      = (global float *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+            global float * dst_data = (global float *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+            dst_data[0] = src[0];
+            dst_data[1] = src[1];
+        }
+    }
+}
+
+kernel void kernel_rope_norm_f16(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * src2,
+        ulong offset2,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3,
+        int n_past,
+        int n_dims,
+        int n_ctx_orig,
+        float freq_base,
+        float freq_scale,
+        float ext_factor,
+        float attn_factor,
+        float beta_fast,
+        float beta_slow
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    src2 = (global float*)((global char*)src2 + offset2);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i3 = get_group_id(2);
+    int i2 = get_group_id(1);
+    int i1 = get_group_id(0);
+
+    float2 corr_dims = rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow);
+
+    global int * pos = src1;
+
+    float theta_base = (float) pos[i2];
+    float inv_ndims = -1.f/n_dims;
+
+    for (int i0 = 2*get_local_id(0); i0 < ne0; i0 += 2*get_local_size(0)) {
+        if (i0 < n_dims) {
+            int ic = i0/2;
+
+            float theta = theta_base * pow(freq_base, inv_ndims*i0);
+
+            float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
+
+            float2 cos_sin_theta = rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor);
+
+            global half * src       = (global half *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+            global half * dst_data  = (global half *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+            float x0 = src[0];
+            float x1 = src[1];
+
+            dst_data[0] = x0*cos_sin_theta.s0 - x1*cos_sin_theta.s1;
+            dst_data[1] = x0*cos_sin_theta.s1 + x1*cos_sin_theta.s0;
+        } else {
+            global half * src      = (global half *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+            global half * dst_data = (global half *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+            dst_data[0] = src[0];
+            dst_data[1] = src[1];
+        }
+    }
+}
+
+kernel void kernel_rope_neox_f32(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * src2,
+        ulong offset2,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3,
+        int n_past,
+        int n_dims,
+        int n_ctx_orig,
+        float freq_base,
+        float freq_scale,
+        float ext_factor,
+        float attn_factor,
+        float beta_fast,
+        float beta_slow
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    src2 = (global float*)((global char*)src2 + offset2);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i3 = get_group_id(2);
+    int i2 = get_group_id(1);
+    int i1 = get_group_id(0);
+
+    float2 corr_dims = rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow);
+
+    global int * pos = src1;
+
+    float theta_base = (float) pos[i2];
+    float inv_ndims = -1.f/n_dims;
+
+    for (int i0 = 2*get_local_id(0); i0 < ne0; i0 += 2*get_local_size(0)) {
+        if (i0 < n_dims) {
+            int ic = i0/2;
+
+            const float theta = theta_base * pow(freq_base, inv_ndims*i0);
+
+            const float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
+
+            float2 cos_sin_theta = rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor);
+
+            global float * src      = (global float *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
+            global float * dst_data = (global float *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + ic*nb0);
+
+            const float x0 = src[0];
+            const float x1 = src[n_dims/2];
+
+            dst_data[0]        = x0*cos_sin_theta.s0 - x1*cos_sin_theta.s1;
+            dst_data[n_dims/2] = x0*cos_sin_theta.s1 + x1*cos_sin_theta.s0;
+        } else {
+            global float * const src = (global float *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+            global float * dst_data  = (global float *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+            dst_data[0] = src[0];
+            dst_data[1] = src[1];
+        }
+    }
+}
+
+kernel void kernel_rope_neox_f16(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * src2,
+        ulong offset2,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3,
+        int n_past,
+        int n_dims,
+        int n_ctx_orig,
+        float freq_base,
+        float freq_scale,
+        float ext_factor,
+        float attn_factor,
+        float beta_fast,
+        float beta_slow
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    src2 = (global float*)((global char*)src2 + offset2);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i3 = get_group_id(2);
+    int i2 = get_group_id(1);
+    int i1 = get_group_id(0);
+
+    float2 corr_dims = rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow);
+
+    global int * pos = src1;
+
+    float theta_base = (float) pos[i2];
+    float inv_ndims = -1.f/n_dims;
+
+    for (int i0 = 2*get_local_id(0); i0 < ne0; i0 += 2*get_local_size(0)) {
+        if (i0 < n_dims) {
+            int ic = i0/2;
+
+            const float theta = theta_base * pow(freq_base, inv_ndims*i0);
+
+            const float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
+
+            float2 cos_sin_theta = rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor);
+
+            global half * src       = (global half *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
+            global half * dst_data  = (global half *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + ic*nb0);
+
+            const float x0 = src[0];
+            const float x1 = src[n_dims/2];
+
+            dst_data[0]        = x0*cos_sin_theta.s0 - x1*cos_sin_theta.s1;
+            dst_data[n_dims/2] = x0*cos_sin_theta.s1 + x1*cos_sin_theta.s0;
+        } else {
+            global half * const src = (global half *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+            global half * dst_data  = (global half *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+            dst_data[0] = src[0];
+            dst_data[1] = src[1];
+        }
+    }
+}
+
+kernel void kernel_rope_multi_f32(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * src2,
+        ulong offset2,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3,
+        int n_past,
+        int n_dims,
+        int n_ctx_orig,
+        float freq_base,
+        float freq_scale,
+        float ext_factor,
+        float attn_factor,
+        float beta_fast,
+        float beta_slow,
+        int4 sections
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    src2 = (global float*)((global char*)src2 + offset2);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i3 = get_group_id(2);
+    int i2 = get_group_id(1);
+    int i1 = get_group_id(0);
+
+    float2 corr_dims = rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow);
+
+    global int * pos = src1;
+
+    const int sect_dims = sections.s0 + sections.s1 + sections.s2 + sections.s3;
+    const int sec_w = sections.s1 + sections.s0;
+
+    float inv_ndims = -1.f/n_dims;
+
+    for (int i0 = 2*get_local_id(0); i0 < ne0; i0 += 2*get_local_size(0)) {
+        if (i0 < n_dims) {
+            int ic = i0/2;
+
+            const int sector = (i0 / 2) % sect_dims;
+            float theta_base = 0.0f;
+
+            if (sector < sections.s0) {
+                theta_base = pos[i2];
+            }
+            else if (sector >= sections.s0 && sector < sec_w) {
+                theta_base = pos[i2 + ne2 * 1];
+            }
+            else if (sector >= sec_w && sector < sec_w + sections.s2) {
+                theta_base = pos[i2 + ne2 * 2];
+            }
+            else if (sector >= sec_w + sections.s2) {
+                theta_base = pos[i2 + ne2 * 3];
+            }
+
+            const float theta = theta_base * pow(freq_base, inv_ndims*i0);
+
+            const float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
+
+            float2 cos_sin_theta = rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor);
+
+            global float * src      = (global float *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
+            global float * dst_data = (global float *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + ic*nb0);
+
+            const float x0 = src[0];
+            const float x1 = src[n_dims/2];
+
+            dst_data[0]        = x0*cos_sin_theta.s0 - x1*cos_sin_theta.s1;
+            dst_data[n_dims/2] = x0*cos_sin_theta.s1 + x1*cos_sin_theta.s0;
+        } else {
+            global float * const src = (global float *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+            global float * dst_data  = (global float *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+            dst_data[0] = src[0];
+            dst_data[1] = src[1];
+        }
+    }
+}
+
+kernel void kernel_rope_multi_f16(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * src2,
+        ulong offset2,
+        global half * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3,
+        int n_past,
+        int n_dims,
+        int n_ctx_orig,
+        float freq_base,
+        float freq_scale,
+        float ext_factor,
+        float attn_factor,
+        float beta_fast,
+        float beta_slow,
+        int4 sections
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    src2 = (global float*)((global char*)src2 + offset2);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i3 = get_group_id(2);
+    int i2 = get_group_id(1);
+    int i1 = get_group_id(0);
+
+    float2 corr_dims = rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow);
+
+    global int * pos = src1;
+
+    const int sect_dims = sections.s0 + sections.s1 + sections.s2 + sections.s3;
+    const int sec_w = sections.s1 + sections.s0;
+
+    float inv_ndims = -1.f/n_dims;
+
+    for (int i0 = 2*get_local_id(0); i0 < ne0; i0 += 2*get_local_size(0)) {
+        if (i0 < n_dims) {
+            int ic = i0/2;
+
+            const int sector = (i0 / 2) % sect_dims;
+            float theta_base = 0.0f;
+
+            if (sector < sections.s0) {
+                theta_base = pos[i2];
+            }
+            else if (sector >= sections.s0 && sector < sec_w) {
+                theta_base = pos[i2 + ne2 * 1];
+            }
+            else if (sector >= sec_w && sector < sec_w + sections.s2) {
+                theta_base = pos[i2 + ne2 * 2];
+            }
+            else if (sector >= sec_w + sections.s2) {
+                theta_base = pos[i2 + ne2 * 3];
+            }
+
+            const float theta = theta_base * pow(freq_base, inv_ndims*i0);
+
+            const float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
+
+            float2 cos_sin_theta = rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor);
+
+            global half * src      = (global half *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
+            global half * dst_data = (global half *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + ic*nb0);
+
+            const float x0 = src[0];
+            const float x1 = src[n_dims/2];
+
+            dst_data[0]        = x0*cos_sin_theta.s0 - x1*cos_sin_theta.s1;
+            dst_data[n_dims/2] = x0*cos_sin_theta.s1 + x1*cos_sin_theta.s0;
+        } else {
+            global half * const src = (global half *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+            global half * dst_data  = (global half *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+            dst_data[0] = src[0];
+            dst_data[1] = src[1];
+        }
+    }
+}
+
+kernel void kernel_rope_vision_f32(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * src2,
+        ulong offset2,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3,
+        int n_past,
+        int n_dims,
+        int n_ctx_orig,
+        float freq_base,
+        float freq_scale,
+        float ext_factor,
+        float attn_factor,
+        float beta_fast,
+        float beta_slow,
+        int4 sections
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    src2 = (global float*)((global char*)src2 + offset2);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i3 = get_group_id(2);
+    int i2 = get_group_id(1);
+    int i1 = get_group_id(0);
+
+    float2 corr_dims = rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow);
+
+    global int * pos = src1;
+
+    const int sect_dims = sections.s0 + sections.s1;
+    const int sec_w = sections.s1 + sections.s0;
+
+    float inv_ndims = -1.f/n_dims;
+
+    for (int i0 = 2*get_local_id(0); i0 < ne0; i0 += 2*get_local_size(0)) {
+        int ic = i0/2;
+
+        const int sector = (i0/2) % sect_dims;
+        float theta_base = 0.0f;
+
+        if (sector < sections.s0) {
+            const int p = sector;
+            theta_base = pos[i2] * pow(freq_base, inv_ndims*2.0f*p);
+        } else if (sector >= sections.s0 && sector < sec_w) {
+            const int p = sector - sections.s0;
+            theta_base = pos[i2 + ne2] * pow(freq_base, inv_ndims*2.0f*p);
+        }
+
+        const float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
+
+        float2 cos_sin_theta = rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor);
+
+        global float * src      = (global float *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
+        global float * dst_data = (global float *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + ic*nb0);
+
+        const float x0 = src[0];
+        const float x1 = src[n_dims];
+
+        dst_data[0]      = x0*cos_sin_theta.s0 - x1*cos_sin_theta.s1;
+        dst_data[n_dims] = x0*cos_sin_theta.s1 + x1*cos_sin_theta.s0;
+    }
+}
+
+kernel void kernel_rope_vision_f16(
+        global void * src0,
+        ulong offset0,
+        global int * src1,
+        ulong offset1,
+        global float * src2,
+        ulong offset2,
+        global half * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne03,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne0,
+        int ne1,
+        int ne2,
+        int ne3,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3,
+        int n_past,
+        int n_dims,
+        int n_ctx_orig,
+        float freq_base,
+        float freq_scale,
+        float ext_factor,
+        float attn_factor,
+        float beta_fast,
+        float beta_slow,
+        int4 sections
+) {
+    src0 = (global void*)((global char*)src0 + offset0);
+    src1 = (global int*)((global char*)src1 + offset1);
+    src2 = (global float*)((global char*)src2 + offset2);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i3 = get_group_id(2);
+    int i2 = get_group_id(1);
+    int i1 = get_group_id(0);
+
+    float2 corr_dims = rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow);
+
+    global int * pos = src1;
+
+    const int sect_dims = sections.s0 + sections.s1;
+    const int sec_w = sections.s1 + sections.s0;
+
+    float inv_ndims = -1.f/n_dims;
+
+    for (int i0 = 2*get_local_id(0); i0 < ne0; i0 += 2*get_local_size(0)) {
+        int ic = i0/2;
+
+        const int sector = (i0/2) % sect_dims;
+        float theta_base = 0.0f;
+
+        if (sector < sections.s0) {
+            const int p = sector;
+            theta_base = pos[i2] * pow(freq_base, inv_ndims*2.0f*p);
+        } else if (sector >= sections.s0 && sector < sec_w) {
+            const int p = sector - sections.s0;
+            theta_base = pos[i2 + ne2] * pow(freq_base, inv_ndims*2.0f*p);
+        }
+
+        const float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
+
+        float2 cos_sin_theta = rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor);
+
+        global half * src      = (global half *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
+        global half * dst_data = (global half *)((global char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + ic*nb0);
+
+        const float x0 = src[0];
+        const float x1 = src[n_dims];
+
+        dst_data[0]      = x0*cos_sin_theta.s0 - x1*cos_sin_theta.s1;
+        dst_data[n_dims] = x0*cos_sin_theta.s1 + x1*cos_sin_theta.s0;
+    }
+}

ggml/src/ggml-opencl/kernels/scale.cl

@@ -0,0 +1,16 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+//------------------------------------------------------------------------------
+// scale
+//------------------------------------------------------------------------------
+kernel void kernel_scale(
+        global float4 * src0,
+        ulong offset0,
+        global float4 * dst,
+        ulong offsetd,
+        float scale
+) {
+    src0 = (global float4*)((global char*)src0 + offset0);
+    dst = (global float4*)((global char*)dst + offsetd);
+    dst[get_global_id(0)] = src0[get_global_id(0)] * scale;
+}

ggml/src/ggml-opencl/kernels/silu.cl

@@ -0,0 +1,30 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+//------------------------------------------------------------------------------
+// silu
+//------------------------------------------------------------------------------
+kernel void kernel_silu(
+        global float * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    float x = src0[get_global_id(0)];
+    dst[get_global_id(0)] = x / (1.0f + exp(-x));
+}
+
+kernel void kernel_silu_4(
+        global float4 * src0,
+        ulong offset0,
+        global float4 * dst,
+        ulong offsetd
+) {
+    src0 = (global float4*)((global char*)src0 + offset0);
+    dst = (global float4*)((global char*)dst + offsetd);
+
+    float4 x = src0[get_global_id(0)];
+    dst[get_global_id(0)] = x / (1.0f + exp(-x));
+}

ggml/src/ggml-opencl/kernels/softmax_4_f16.cl

@@ -0,0 +1,87 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_soft_max_4_f16(
+        global float * src0,
+        ulong offset0,
+        global half * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        float scale,
+        float max_bias,
+        float m0,
+        float m1,
+        int n_head_log2
+) {
+    src0 = (global float *)((global char *)src0 + offset0);
+    src1 = (global half *)((global char *)src1 + offset1);
+    dst = (global float *)((global char *)dst + offsetd);
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    global float4 * psrc4 = (global float4 *)(src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
+    global half4  * pmask = (global char *)src1 != (global char *)src0 ? (global half4 *)(src1 + i01*ne00) : 0;
+    global float4 * pdst4 = (global float4 *)(dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
+
+    float slope = 1.0f;
+
+    // ALiBi
+    if (max_bias > 0.0f) {
+        int h = i02;
+
+        float base = h < n_head_log2 ? m0 : m1;
+        int   exp  = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+        slope = pow(base, exp);
+    }
+
+    // parallel max
+    float4 lmax4 = -INFINITY;
+    for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
+        lmax4 = fmax(lmax4, psrc4[i00]*scale + slope*(pmask ? convert_float4(pmask[i00]) : 0.0f));
+    }
+    float lmax = fmax(fmax(lmax4.s0, lmax4.s1), fmax(lmax4.s2, lmax4.s3));
+
+    const float max = sub_group_reduce_max(lmax);
+
+    // parallel sum
+    float4 lsum4 = 0.0f;
+    for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
+        const float4 exp_psrc4 = exp((psrc4[i00]*scale + slope*(pmask ? convert_float4(pmask[i00]) : 0.0f)) - max);
+        lsum4 += exp_psrc4;
+        pdst4[i00] = exp_psrc4;
+    }
+    float lsum = lsum4.s0 + lsum4.s1 + lsum4.s2 + lsum4.s3;
+
+    const float sum = sub_group_reduce_add(lsum);
+
+    for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
+        pdst4[i00] /= sum;
+    }
+}

ggml/src/ggml-opencl/kernels/softmax_4_f32.cl

@@ -0,0 +1,87 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_soft_max_4(
+        global float * src0,
+        ulong offset0,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        float scale,
+        float max_bias,
+        float m0,
+        float m1,
+        int n_head_log2
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    global float4 * psrc4 = (global float4 *)(src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
+    global float4 * pmask = src1 != src0 ? (global float4 *)(src1 + i01*ne00) : 0;
+    global float4 * pdst4 = (global float4 *)(dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
+
+    float slope = 1.0f;
+
+    // ALiBi
+    if (max_bias > 0.0f) {
+        int h = i02;
+
+        float base = h < n_head_log2 ? m0 : m1;
+        int   exp  = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+        slope = pow(base, exp);
+    }
+
+    // parallel max
+    float4 lmax4 = -INFINITY;
+    for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
+        lmax4 = fmax(lmax4, psrc4[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f));
+    }
+    float lmax = fmax(fmax(lmax4.s0, lmax4.s1), fmax(lmax4.s2, lmax4.s3));
+
+    const float max = sub_group_reduce_max(lmax);
+
+    // parallel sum
+    float4 lsum4 = 0.0f;
+    for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
+        const float4 exp_psrc4 = exp((psrc4[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f)) - max);
+        lsum4 += exp_psrc4;
+        pdst4[i00] = exp_psrc4;
+    }
+    float lsum = lsum4.s0 + lsum4.s1 + lsum4.s2 + lsum4.s3;
+
+    const float sum = sub_group_reduce_add(lsum);
+
+    for (int i00 = get_local_id(0); i00 < ne00/4; i00 += get_local_size(0)) {
+        pdst4[i00] /= sum;
+    }
+}

ggml/src/ggml-opencl/kernels/softmax_f16.cl

@@ -0,0 +1,86 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_soft_max_f16(
+        global float * src0,
+        ulong offset0,
+        global half * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        float scale,
+        float max_bias,
+        float m0,
+        float m1,
+        int n_head_log2
+) {
+    src0 = (global float *)((global char *)src0 + offset0);
+    src1 = (global half *)((global char *)src1 + offset1);
+    dst = (global float *)((global char *)dst + offsetd);
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    global float * psrc0 = src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+    global half  * pmask = (global char *)src1 != (global char *)src0 ? src1 + i01*ne00 : 0;
+    global float * pdst  = dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+
+    float slope = 1.0f;
+
+    // ALiBi
+    if (max_bias > 0.0f) {
+        int h = i02;
+
+        float base = h < n_head_log2 ? m0 : m1;
+        int   exp  = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+        slope = pow(base, exp);
+    }
+
+    // parallel max
+    float lmax = -INFINITY;
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        lmax = fmax(lmax, psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f));
+    }
+    float max = sub_group_reduce_max(lmax);
+
+    // parallel sum
+    float lsum = 0.0f;
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f)) - max);
+        lsum += exp_psrc0;
+        // Remember the result of exp here. exp is expensive, so we really do not
+        // wish to compute it twice.
+        pdst[i00] = exp_psrc0;
+    }
+
+    const float sum = sub_group_reduce_add(lsum);
+
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        pdst[i00] /= sum;
+    }
+}

ggml/src/ggml-opencl/kernels/softmax_f32.cl

@@ -0,0 +1,86 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_soft_max(
+        global float * src0,
+        ulong offset0,
+        global float * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        float scale,
+        float max_bias,
+        float m0,
+        float m1,
+        int n_head_log2
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    src1 = (global float*)((global char*)src1 + offset1);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    global float * psrc0 = src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+    global float * pmask = src1 != src0 ? src1 + i01*ne00 : 0;
+    global float * pdst  = dst  + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+
+    float slope = 1.0f;
+
+    // ALiBi
+    if (max_bias > 0.0f) {
+        int h = i02;
+
+        float base = h < n_head_log2 ? m0 : m1;
+        int   exp  = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
+
+        slope = pow(base, exp);
+    }
+
+    // parallel max
+    float lmax = -INFINITY;
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        lmax = fmax(lmax, psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f));
+    }
+    float max = sub_group_reduce_max(lmax);
+
+    // parallel sum
+    float lsum = 0.0f;
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f)) - max);
+        lsum += exp_psrc0;
+        // Remember the result of exp here. exp is expensive, so we really do not
+        // wish to compute it twice.
+        pdst[i00] = exp_psrc0;
+    }
+
+    const float sum = sub_group_reduce_add(lsum);
+
+    for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
+        pdst[i00] /= sum;
+    }
+}

ggml/src/ggml-opencl/kernels/transpose.cl

@@ -0,0 +1,84 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+// 16-bit transpose, loading/storing a 4x4 tile of elements
+kernel void kernel_transpose_16(
+    __read_only image1d_buffer_t input,
+    __write_only image1d_buffer_t output,
+    const uint rows,
+    const uint cols
+) {
+
+    const int i = get_global_id(0);
+    const int j = get_global_id(1);
+    const int i_2 = i<<2;
+    const int j_2 = j<<2;
+
+    half4 temp0 = read_imageh(input, (j_2+0)*cols+i);
+    half4 temp1 = read_imageh(input, (j_2+1)*cols+i);
+    half4 temp2 = read_imageh(input, (j_2+2)*cols+i);
+    half4 temp3 = read_imageh(input, (j_2+3)*cols+i);
+
+    write_imageh(output, (i_2+0)*rows+j, (half4)(temp0.s0, temp1.s0, temp2.s0, temp3.s0));
+    write_imageh(output, (i_2+1)*rows+j, (half4)(temp0.s1, temp1.s1, temp2.s1, temp3.s1));
+    write_imageh(output, (i_2+2)*rows+j, (half4)(temp0.s2, temp1.s2, temp2.s2, temp3.s2));
+    write_imageh(output, (i_2+3)*rows+j, (half4)(temp0.s3, temp1.s3, temp2.s3, temp3.s3));
+}
+
+// 32-bit transpose, loading/storing a 4x4 tile of elements
+kernel void kernel_transpose_32(
+    __read_only image1d_buffer_t input,
+    __write_only image1d_buffer_t output,
+    const uint rows,
+    const uint cols
+) {
+
+    const int i = get_global_id(0);
+    const int j = get_global_id(1);
+    const int i_2 = i<<2;
+    const int j_2 = j<<2;
+
+    float4 temp0 = read_imagef(input, (j_2+0)*cols+i);
+    float4 temp1 = read_imagef(input, (j_2+1)*cols+i);
+    float4 temp2 = read_imagef(input, (j_2+2)*cols+i);
+    float4 temp3 = read_imagef(input, (j_2+3)*cols+i);
+
+    write_imagef(output, (i_2+0)*rows+j, (float4)(temp0.s0, temp1.s0, temp2.s0, temp3.s0));
+    write_imagef(output, (i_2+1)*rows+j, (float4)(temp0.s1, temp1.s1, temp2.s1, temp3.s1));
+    write_imagef(output, (i_2+2)*rows+j, (float4)(temp0.s2, temp1.s2, temp2.s2, temp3.s2));
+    write_imagef(output, (i_2+3)*rows+j, (float4)(temp0.s3, temp1.s3, temp2.s3, temp3.s3));
+
+}
+
+// 32-bit transpose, loading/storing a 4x4 tile of elements
+// Only used for activations
+// converts to FP16
+// also adds zero padding for non multiple of 8 prompt lengths
+kernel void kernel_transpose_32_16(__read_only image1d_buffer_t input, __write_only image1d_buffer_t output, const uint rows, const uint cols, const uint padded_rows) {
+
+    const int i = get_global_id(0);
+    const int j = get_global_id(1);
+    const int i_2 = i<<2;
+    const int j_2 = j<<2;
+    half4 temp0 = {0,0,0,0}; // initialize outputs to 0
+    half4 temp1 = {0,0,0,0};
+    half4 temp2 = {0,0,0,0};
+    half4 temp3 = {0,0,0,0};
+
+    if((j_2+0)*cols+i*4+3 < rows*cols*16){ // only load from a valid location. Otherwise keep register data as 0
+        temp0 = read_imageh(input, (j_2+0)*cols+i);
+    }
+    if((j_2+1)*cols+i*4+3 < rows*cols*16){
+        temp1 = read_imageh(input, (j_2+1)*cols+i);
+    }
+    if((j_2+2)*cols+i*4+3 < rows*cols*16){
+        temp2 = read_imageh(input, (j_2+2)*cols+i);
+    }
+    if((j_2+3)*cols+i*4+3 < rows*cols*16){
+        temp3 = read_imageh(input, (j_2+3)*cols+i);
+    }
+
+    write_imageh(output, (i_2+0)*padded_rows+j, (half4)(temp0.s0, temp1.s0, temp2.s0, temp3.s0)); // no conditionals for output, includes zero padding
+    write_imageh(output, (i_2+1)*padded_rows+j, (half4)(temp0.s1, temp1.s1, temp2.s1, temp3.s1));
+    write_imageh(output, (i_2+2)*padded_rows+j, (half4)(temp0.s2, temp1.s2, temp2.s2, temp3.s2));
+    write_imageh(output, (i_2+3)*padded_rows+j, (half4)(temp0.s3, temp1.s3, temp2.s3, temp3.s3));
+}

ggml/src/ggml-rpc/ggml-rpc.cpp

@@ -1,6 +1,7 @@
 #include "ggml-rpc.h"
 #include "ggml-impl.h"
 #include "ggml-backend-impl.h"
+#include "ggml-cpp.h"
 
 #include <cinttypes>
 #include <string>
@@ -853,12 +854,13 @@ bool rpc_server::get_alloc_size(const rpc_msg_get_alloc_size_req & request, rpc_
         /*.no_alloc   =*/ true,
     };
 
-    struct ggml_context * ctx = ggml_init(params);
+    ggml_context_ptr ctx_ptr { ggml_init(params) };
+    GGML_ASSERT(ctx_ptr != nullptr);
+    ggml_context * ctx = ctx_ptr.get();
     ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor);
 
     if (tensor == nullptr) {
         GGML_LOG_ERROR("Null tensor pointer passed to server get_alloc_size function.\n");
-        ggml_free(ctx);
         return false;
     }
 
@@ -871,7 +873,6 @@ bool rpc_server::get_alloc_size(const rpc_msg_get_alloc_size_req & request, rpc_
 
     response.alloc_size = ggml_backend_buft_get_alloc_size(buft,tensor);
 
-    ggml_free(ctx);
     return true;
 }
 
@@ -985,11 +986,12 @@ bool rpc_server::set_tensor(const std::vector<uint8_t> & input) {
         /*.mem_buffer =*/ NULL,
         /*.no_alloc   =*/ true,
     };
-    struct ggml_context * ctx = ggml_init(params);
+    ggml_context_ptr ctx_ptr { ggml_init(params) };
+    GGML_ASSERT(ctx_ptr != nullptr);
+    ggml_context * ctx = ctx_ptr.get();
     ggml_tensor * tensor = deserialize_tensor(ctx, in_tensor);
     if (tensor == nullptr) {
         GGML_LOG_ERROR("[%s] error deserializing tensor\n", __func__);
-        ggml_free(ctx);
         return false;
     }
     GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %zu\n", __func__, (void*)tensor->buffer, tensor->data, offset, size);
@@ -1016,7 +1018,6 @@ bool rpc_server::set_tensor(const std::vector<uint8_t> & input) {
         printf("[%s] saved to '%s'\n", __func__, cache_file.c_str());
     }
     ggml_backend_tensor_set(tensor, data, offset, size);
-    ggml_free(ctx);
     return true;
 }
 
@@ -1060,11 +1061,12 @@ bool rpc_server::set_tensor_hash(const std::vector<uint8_t> & input, rpc_msg_set
         /*.mem_buffer =*/ NULL,
         /*.no_alloc   =*/ true,
     };
-    struct ggml_context * ctx = ggml_init(params);
+    ggml_context_ptr ctx_ptr { ggml_init(params) };
+    GGML_ASSERT(ctx_ptr != nullptr);
+    ggml_context * ctx = ctx_ptr.get();
     ggml_tensor * tensor = deserialize_tensor(ctx, in_tensor);
     if (tensor == nullptr) {
         GGML_LOG_ERROR("[%s] error deserializing tensor\n", __func__);
-        ggml_free(ctx);
         return false;
     }
     GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %zu, hash: %" PRIx64 "\n", __func__, (void*)tensor->buffer, tensor->data, offset, size, *hash);
@@ -1080,7 +1082,6 @@ bool rpc_server::set_tensor_hash(const std::vector<uint8_t> & input, rpc_msg_set
     }
     ggml_backend_tensor_set(tensor, cached_file.data(), offset, size);
     response.result = 1;
-    ggml_free(ctx);
     return true;
 }
 
@@ -1090,11 +1091,12 @@ bool rpc_server::init_tensor(const rpc_msg_init_tensor_req & request) {
         /*.mem_buffer =*/ NULL,
         /*.no_alloc   =*/ true,
     };
-    struct ggml_context * ctx = ggml_init(params);
+    ggml_context_ptr ctx_ptr { ggml_init(params) };
+    GGML_ASSERT(ctx_ptr != nullptr);
+    ggml_context * ctx = ctx_ptr.get();
     ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor);
     if (tensor == nullptr) {
         GGML_LOG_ERROR("Null tensor pointer passed to server init_tensor function.\n");
-        ggml_free(ctx);
         return false;
     }
 
@@ -1110,11 +1112,9 @@ bool rpc_server::init_tensor(const rpc_msg_init_tensor_req & request) {
         // This pointer can either be passed around client/server, or probably better stored server-side and kept track of.
         // Currently unimplemented.
         GGML_LOG_ERROR("tensor->extra populated by the backend, this is currently unsupported.\n");
-        ggml_free(ctx);
         return false;
     }
 
-    ggml_free(ctx);
     return true;
 }
 
@@ -1124,11 +1124,12 @@ bool rpc_server::get_tensor(const rpc_msg_get_tensor_req & request, std::vector<
         /*.mem_buffer =*/ NULL,
         /*.no_alloc   =*/ true,
     };
-    struct ggml_context * ctx = ggml_init(params);
+    ggml_context_ptr ctx_ptr { ggml_init(params) };
+    GGML_ASSERT(ctx_ptr != nullptr);
+    ggml_context * ctx = ctx_ptr.get();
     ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor);
     if (tensor == nullptr) {
         GGML_LOG_ERROR("[%s] error deserializing tensor\n", __func__);
-        ggml_free(ctx);
         return false;
     }
     GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %" PRIu64 "\n", __func__, (void*)tensor->buffer, tensor->data, request.offset, request.size);
@@ -1147,7 +1148,6 @@ bool rpc_server::get_tensor(const rpc_msg_get_tensor_req & request, std::vector<
 
     response.resize(request.size, 0);
     ggml_backend_tensor_get(tensor, response.data(), request.offset, request.size);
-    ggml_free(ctx);
     return true;
 }
 
@@ -1157,12 +1157,14 @@ bool rpc_server::copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_co
         /*.mem_buffer =*/ NULL,
         /*.no_alloc   =*/ true,
     };
-    struct ggml_context * ctx = ggml_init(params);
+    ggml_context_ptr ctx_ptr { ggml_init(params) };
+    GGML_ASSERT(ctx_ptr != nullptr);
+    ggml_context * ctx = ctx_ptr.get();
+
     ggml_tensor * src = deserialize_tensor(ctx, &request.src);
     ggml_tensor * dst = deserialize_tensor(ctx, &request.dst);
     if (src == nullptr || dst == nullptr) {
         GGML_LOG_ERROR("[%s] error deserializing tensors\n", __func__);
-        ggml_free(ctx);
         return false;
     }
 
@@ -1180,7 +1182,6 @@ bool rpc_server::copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_co
                          dst_data + src_size,
                          dst_base,
                          dst_base + dst_buf_sz);
-        ggml_free(ctx);
         return false;
     }
 
@@ -1188,7 +1189,6 @@ bool rpc_server::copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_co
                      __func__, (void*) src->buffer, (void*) dst->buffer);
 
     response.result = ggml_backend_buffer_copy_tensor(src, dst);
-    ggml_free(ctx);
     return true;
 }
 
@@ -1242,7 +1242,9 @@ bool rpc_server::graph_compute(const std::vector<uint8_t> & input, rpc_msg_graph
         /*.mem_buffer =*/ NULL,
         /*.no_alloc   =*/ true,
     };
-    struct ggml_context * ctx = ggml_init(params);
+    ggml_context_ptr ctx_ptr { ggml_init(params) };
+    GGML_ASSERT(ctx_ptr != nullptr);
+    ggml_context * ctx = ctx_ptr.get();
     struct ggml_cgraph * graph = ggml_new_graph_custom(ctx, n_nodes, false);
     graph->n_nodes = n_nodes;
     std::unordered_map<uint64_t, const rpc_tensor*> tensor_ptrs;
@@ -1257,7 +1259,6 @@ bool rpc_server::graph_compute(const std::vector<uint8_t> & input, rpc_msg_graph
     }
     ggml_status status = ggml_backend_graph_compute(backend, graph);
     response.result = status;
-    ggml_free(ctx);
     return true;
 }
 

ggml/src/ggml-sycl/element_wise.hpp

@@ -2,6 +2,13 @@
 #define GGML_SYCL_ELEMENTWISE_HPP
 
 #include "common.hpp"
+#include "ggml.h"
+#include <limits.h>
+
+template <typename T>
+T neg_infinity() {
+    return -std::numeric_limits<T>::infinity();
+}
 
 static __dpct_inline__ float op_repeat(const float a, const float b) {
     return b;
@@ -24,6 +31,19 @@ static __dpct_inline__ float op_div(const float a, const float b) {
     return a / b;
 }
 
+template<typename T>
+struct typed_data {
+    const T * src;
+    T * dst;
+};
+
+template<typename T>
+typed_data<T> cast_data(ggml_tensor * dst) {
+    return {
+        /* .src = */ static_cast<const T *>(dst->src[0]->data),
+        /* .dst = */ static_cast<T *>(dst->data)
+    };
+}
 
 void ggml_sycl_sqrt(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
@@ -65,6 +85,10 @@ void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
 void ggml_sycl_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
+void ggml_sycl_clamp(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
+// ---------
+
 void ggml_sycl_add(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
 void ggml_sycl_sub(ggml_backend_sycl_context & ctx, ggml_tensor * dst);

ggml/src/ggml-sycl/ggml-sycl.cpp

@@ -1617,17 +1617,6 @@ static void scale_f32(const float * x, float * dst, const float scale, const int
     dst[i] = scale * x[i];
 }
 
-static void clamp_f32(const float * x, float * dst, const float min, const float max, const int k,
-                      const sycl::nd_item<3> &item_ct1) {
-    const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                  item_ct1.get_local_id(2);
-
-    if (i >= k) {
-        return;
-    }
-
-    dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]);
-}
 
 template <typename Ti, typename To>
 static  void pool2d_nchw_kernel(
@@ -1768,18 +1757,6 @@ static void scale_f32_sycl(const float *x, float *dst, const float scale,
         });
 }
 
-static void clamp_f32_sycl(const float *x, float *dst, const float min,
-                           const float max, const int k,
-                           queue_ptr stream) {
-    const int num_blocks = (k + SYCL_CLAMP_BLOCK_SIZE - 1) / SYCL_CLAMP_BLOCK_SIZE;
-    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
-                              sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            clamp_f32(x, dst, min, max, k, item_ct1);
-        });
-}
 
 static void sum_rows_f32_sycl(const float *x, float *dst, const int ncols,
                               const int nrows, queue_ptr stream) {
@@ -2258,26 +2235,6 @@ inline void ggml_sycl_op_scale(ggml_backend_sycl_context & ctx, ggml_tensor *dst
     SYCL_CHECK(0);
 }
 
-inline void ggml_sycl_op_clamp(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
-
-    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-    const float * src0_dd = static_cast<const float *>(dst->src[0]->data);
-    float *       dst_dd  = static_cast<float *>(dst->data);
-
-    float min;
-    float max;
-    memcpy(&min, dst->op_params, sizeof(float));
-    memcpy(&max, (float *) dst->op_params + 1, sizeof(float));
-
-    clamp_f32_sycl(src0_dd, dst_dd, min, max, ggml_nelements(dst->src[0]), ctx.stream());
-    /*
-    DPCT1010:88: SYCL uses exceptions to report errors and does not use the
-    error codes. The call was replaced with 0. You need to rewrite this code.
-    */
-    SYCL_CHECK(0);
-}
-
 static void ggml_sycl_set_peer_access(const int n_tokens, int main_device) {
     static bool peer_access_enabled = false;
 
@@ -3218,10 +3175,6 @@ static void ggml_sycl_scale(ggml_backend_sycl_context & ctx, ggml_tensor * dst)
     ggml_sycl_op_scale(ctx, dst);
 }
 
-static void ggml_sycl_clamp(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    ggml_sycl_op_clamp(ctx, dst);
-}
-
 static void ggml_sycl_diag_mask_inf(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     ggml_sycl_op_diag_mask_inf(ctx, dst);
 }
@@ -3700,7 +3653,8 @@ static ggml_status ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_
 
 #ifdef GGML_SYCL_GRAPH
     if (!g_ggml_sycl_disable_graph) {
-        if (!sycl_ctx->exec_graph && !dpct::get_device(sycl_ctx->device).has(sycl::aspect::ext_oneapi_graph)) {
+        const bool graph_support = dpct::get_device(sycl_ctx->device).has(sycl::aspect::ext_oneapi_limited_graph);
+        if (!graph_support) {
             GGML_SYCL_DEBUG("[SYCL-GRAPH] can not use graphs on device:%d\n", sycl_ctx->device);
             ggml_backend_sycl_graph_compute_impl(sycl_ctx, cgraph);
             return GGML_STATUS_SUCCESS;
@@ -3711,8 +3665,10 @@ static ggml_status ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_
         ggml_backend_sycl_graph_compute_impl(sycl_ctx, cgraph);
         model_sycl_graph.end_recording();
 
-        if (!sycl_ctx->exec_graph) {
-            auto exec_graph = model_sycl_graph.finalize({sycl_ex::property::graph::updatable{}});
+        const bool graph_update_support = dpct::get_device(sycl_ctx->device).has(sycl::aspect::ext_oneapi_graph);
+        if (!sycl_ctx->exec_graph || !graph_update_support) {
+            auto exec_graph = graph_update_support ? model_sycl_graph.finalize(sycl_ex::property::graph::updatable{}) :
+                                                     model_sycl_graph.finalize();
             sycl_ctx->exec_graph = std::make_unique<
                 sycl_ex::command_graph<sycl_ex::graph_state::executable>>(exec_graph);
         } else {
@@ -3900,7 +3856,11 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
                 case GGML_UNARY_OP_GELU_QUICK:
                 case GGML_UNARY_OP_TANH:
                 case GGML_UNARY_OP_EXP:
-                    return ggml_is_contiguous(op->src[0]) && (op->src[0]->type == GGML_TYPE_F32);
+#if defined (GGML_SYCL_F16)
+                    return ggml_is_contiguous(op->src[0]) && (op->type == op->src[0]->type);
+#else
+                    return ggml_is_contiguous(op->src[0]) && (op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32) && (op->type == op->src[0]->type);
+#endif
                 default:
                     return false;
             }
@@ -4022,13 +3982,18 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_SUB:
         case GGML_OP_MUL:
         case GGML_OP_DIV:
+            return (op->src[0]->type == GGML_TYPE_F32);
         case GGML_OP_SQR:
         case GGML_OP_SQRT:
         case GGML_OP_SIN:
         case GGML_OP_COS:
         case GGML_OP_CLAMP:
         case GGML_OP_LOG:
-            return (op->src[0]->type == GGML_TYPE_F32);
+#if defined (GGML_SYCL_F16)
+            return ((op->type == GGML_TYPE_F32 || op->type == GGML_SYCL_F16) && (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_SYCL_F16) && (op->type == op->src[0]->type));
+#else
+            return (op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32) && (op->type == op->src[0]->type);
+#endif
         case GGML_OP_NORM:
         case GGML_OP_RMS_NORM:
         case GGML_OP_L2_NORM:
@@ -4044,23 +4009,21 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_ROPE:
             {
                 const int mode = ((const int32_t *) op->op_params)[2];
-                if (mode & GGML_ROPE_TYPE_MROPE) {
-                    return false;
-                }
-                if (mode & GGML_ROPE_TYPE_VISION) {
+                // mode is not used as a bitmask in practice, the various rope type modes are independent implementations
+                if (mode == GGML_ROPE_TYPE_MROPE) {
                     return false;
                 }
                 return ggml_is_contiguous(op->src[0]);
             }
         case GGML_OP_IM2COL:
-            // TODO: add support for the new F32 operations
-            return op->src[0]->type == GGML_TYPE_F16;
+            return true;
+        case GGML_OP_UPSCALE:
+            return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST;
         case GGML_OP_POOL_2D:
         case GGML_OP_SUM:
         case GGML_OP_SUM_ROWS:
         case GGML_OP_ARGSORT:
         case GGML_OP_ACC:
-        case GGML_OP_UPSCALE:
         case GGML_OP_PAD:
         case GGML_OP_LEAKY_RELU:
         case GGML_OP_TIMESTEP_EMBEDDING:

ggml/src/ggml-sycl/im2col.cpp

@@ -12,110 +12,125 @@
 
 #include "im2col.hpp"
 
+#include <sycl/sycl.hpp>
+#include <type_traits>  // For std::is_same_v
+
+#include "ggml.h"
+
 template <typename T>
-static void im2col_kernel(
-        const float *x, T *dst, int64_t batch_offset, int64_t offset_delta,
-        int64_t IC, int64_t IW, int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH,
-        int64_t pelements, int64_t CHW, int s0, int s1, int p0, int p1, int d0, int d1,
-        const sycl::nd_item<3> &item_ct1) {
+static void im2col_kernel(const float * x, T * dst, int64_t batch_offset, int64_t offset_delta, int64_t IC, int64_t IW,
+                          int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH, int64_t pelements, int64_t CHW,
+                          int s0, int s1, int p0, int p1, int d0, int d1, const sycl::nd_item<3> & item_ct1) {
     const int64_t work_group_size = item_ct1.get_local_range(2);
-    const int64_t global_id = item_ct1.get_local_id(2) + work_group_size * item_ct1.get_group(2);
+    const int64_t global_id       = item_ct1.get_local_id(2) + (work_group_size * item_ct1.get_group(2));
 
     // make each work-item deal with more elements since sycl global range can not exceed max int
-    for (int64_t i = global_id; i < pelements; i += work_group_size * item_ct1.get_group_range(2)) {
-
+    for (int64_t i = global_id; i < pelements; i += (work_group_size * item_ct1.get_group_range(2))) {
         const int64_t ksize = OW * (KH > 1 ? KW : 1);
-        const int64_t kx = i / ksize;
-        const int64_t kd = kx * ksize;
-        const int64_t ky = (i - kd) / OW;
-        const int64_t ix = i % OW;
+        const int64_t kx    = i / ksize;
+        const int64_t kd    = kx * ksize;
+        const int64_t ky    = (i - kd) / OW;
+        const int64_t ix    = i % OW;
 
-        const int64_t  oh = item_ct1.get_group(1);
-        const int64_t  batch = item_ct1.get_group(0) / IC;
-        const int64_t  ic = item_ct1.get_group(0) % IC;
+        const int64_t oh    = item_ct1.get_group(1);
+        const int64_t batch = item_ct1.get_group(0) / IC;
+        const int64_t ic    = item_ct1.get_group(0) % IC;
 
-        const int64_t iiw = ix * s0 + kx * d0 - p0;
-        const int64_t iih = oh * s1 + ky * d1 - p1;
+        const int64_t iiw = (ix * s0) + (kx * d0) - p0;
+        const int64_t iih = (oh * s1) + (ky * d1) - p1;
 
-        const int64_t offset_dst =
-            ((batch * OH + oh) * OW + ix) * CHW +
-            (ic * (KW * KH) + ky * KW + kx);
+        const int64_t offset_dst = (((batch * OH + oh) * OW + ix) * CHW) + (ic * (KW * KH) + ky * KW + kx);
 
-        if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
-            dst[offset_dst] =
-                sycl::vec<float, 1>(0.0f)
-                    .convert<sycl::half, sycl::rounding_mode::automatic>()[0];
-        } else {
-            const int64_t offset_src = ic * offset_delta + batch * batch_offset;
-            dst[offset_dst] =
-                sycl::vec<float, 1>(x[offset_src + iih * IW + iiw])
-                    .convert<sycl::half, sycl::rounding_mode::automatic>()[0];
+        const int64_t offset_src_base = (ic * offset_delta) + (batch * batch_offset);
+        const int64_t offset_src      = offset_src_base + (iih * IW) + iiw;
+
+        const bool  out_of_bounds = (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW);
+        const float src_val       = out_of_bounds ? 0.0f : x[offset_src];
+
+        if constexpr (std::is_same_v<T, sycl::half>) {
+            dst[offset_dst] = sycl::half(src_val);
+        } else if constexpr (std::is_same_v<T, float>) {
+            dst[offset_dst] = src_val;
         }
     }
 }
 
 template <typename T>
-static void im2col_sycl(
-        const float *x, T *dst, int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW,
-        int64_t KH, int64_t IC, int64_t batch, int64_t batch_offset, int64_t offset_delta,
-        int s0, int s1, int p0, int p1, int d0, int d1,
-        queue_ptr stream) {
+static void im2col_sycl_internal(const float * x, T * dst, int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW,
+                                 int64_t KH, int64_t IC, int64_t batch, int64_t batch_offset, int64_t offset_delta,
+                                 int s0, int s1, int p0, int p1, int d0, int d1, queue_ptr stream) {
     const int64_t parallel_elements = OW * KW * KH;
-    const int64_t num_blocks = (parallel_elements + SYCL_IM2COL_BLOCK_SIZE - 1) / SYCL_IM2COL_BLOCK_SIZE;
+    const int64_t num_blocks        = (parallel_elements + SYCL_IM2COL_BLOCK_SIZE - 1) / SYCL_IM2COL_BLOCK_SIZE;
 
     // decrease global range when it exceeds the max int
     int64_t local_size = downsample_sycl_global_range(batch * IC * OH * num_blocks, SYCL_IM2COL_BLOCK_SIZE);
+
     sycl::range<3> block_nums(batch * IC, OH, num_blocks);
     sycl::range<3> local_range(1, 1, local_size);
 
-    {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
+    const int64_t CHW = IC * KH * KW;
 
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * local_range, local_range),
-            [=](sycl::nd_item<3> item_ct1) {
-                im2col_kernel(x, dst, batch_offset, offset_delta, IC, IW, IH, OH, OW, KW, KH,
-                               parallel_elements, (IC * KH * KW), s0, s1, p0,
-                               p1, d0, d1, item_ct1);
-            });
-    }
+    stream->parallel_for(sycl::nd_range<3>(block_nums * local_range, local_range), [=](sycl::nd_item<3> item_ct1) {
+        im2col_kernel<T>(x, dst, batch_offset, offset_delta, IC, IW, IH, OH, OW, KW, KH, parallel_elements, CHW, s0, s1,
+                         p0, p1, d0, d1, item_ct1);
+    });
 }
 
-void ggml_sycl_op_im2col(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
+static void im2col_sycl_f16(const float * x, sycl::half * dst, int64_t IW, int64_t IH, int64_t OW, int64_t OH,
+                            int64_t KW, int64_t KH, int64_t IC, int64_t batch, int64_t batch_offset,
+                            int64_t offset_delta, int s0, int s1, int p0, int p1, int d0, int d1, queue_ptr stream) {
+    if (!stream->get_device().has(sycl::aspect::fp16)) {
+        throw sycl::exception(sycl::make_error_code(sycl::errc::kernel_not_supported),
+                              "Device does not support half precision (fp16) operations!");
+    }
+    im2col_sycl_internal<sycl::half>(x, dst, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, offset_delta, s0, s1, p0,
+                                     p1, d0, d1, stream);
+}
+
+static void im2col_sycl_f32(const float * x, float * dst, int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW,
+                            int64_t KH, int64_t IC, int64_t batch, int64_t batch_offset, int64_t offset_delta, int s0,
+                            int s1, int p0, int p1, int d0, int d1, queue_ptr stream) {
+    im2col_sycl_internal<float>(x, dst, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, offset_delta, s0, s1, p0, p1,
+                                d0, d1, stream);
+}
+
+void ggml_sycl_op_im2col(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
 
-    GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT(dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
 
-    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
-    const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
-    const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
-    const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
-    const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
-    const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
+    const int32_t s0 = ((const int32_t *) (dst->op_params))[0];
+    const int32_t s1 = ((const int32_t *) (dst->op_params))[1];
+    const int32_t p0 = ((const int32_t *) (dst->op_params))[2];
+    const int32_t p1 = ((const int32_t *) (dst->op_params))[3];
+    const int32_t d0 = ((const int32_t *) (dst->op_params))[4];
+    const int32_t d1 = ((const int32_t *) (dst->op_params))[5];
 
-    const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
+    const bool is_2D = ((const int32_t *) (dst->op_params))[6] == 1;
 
     const int64_t IC = src1->ne[is_2D ? 2 : 1];
     const int64_t IH = is_2D ? src1->ne[1] : 1;
-    const int64_t IW =         src1->ne[0];
+    const int64_t IW = src1->ne[0];
 
     const int64_t KH = is_2D ? src0->ne[1] : 1;
-    const int64_t KW =         src0->ne[0];
+    const int64_t KW = src0->ne[0];
 
     const int64_t OH = is_2D ? dst->ne[2] : 1;
-    const int64_t OW =         dst->ne[1];
+    const int64_t OW = dst->ne[1];
 
-    const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
-    const int64_t batch = src1->ne[3];
-    const size_t batch_offset = src1->nb[3] / 4; // nb is byte offset, src is type float32
+    const size_t  delta_offset = src1->nb[is_2D ? 2 : 1] / sizeof(float);
+    const int64_t batch        = src1->ne[is_2D ? 3 : 2];
+    const size_t  batch_offset = src1->nb[is_2D ? 3 : 2] / sizeof(float);
+
+    queue_ptr stream = ctx.stream();
 
     if (dst->type == GGML_TYPE_F16) {
-        im2col_sycl((const float *) src1->data, (sycl::half *)dst->data, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, ctx.stream());
+        im2col_sycl_f16((const float *) src1->data, (sycl::half *) dst->data, IW, IH, OW, OH, KW, KH, IC, batch,
+                        batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream);
     } else {
-        im2col_sycl((const float *) src1->data, (float *)dst->data, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, ctx.stream());
+        im2col_sycl_f32((const float *) src1->data, (float *) dst->data, IW, IH, OW, OH, KW, KH, IC, batch,
+                        batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream);
     }
 }

ggml/src/ggml-sycl/rope.cpp

@@ -1,9 +1,15 @@
 #include "rope.hpp"
+#include "ggml-sycl/common.hpp"
+#include "ggml.h"
 
 struct rope_corr_dims {
     float v[2];
 };
 
+struct mrope_sections {
+    int v[4];
+};
+
 static float rope_yarn_ramp(const float low, const float high, const int i0) {
     const float y = (i0 / 2 - low) / sycl::max(0.001f, high - low);
     return 1.0f - sycl::min(1.0f, sycl::max(0.0f, y));
@@ -114,6 +120,48 @@ static void rope_neox(
     dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
 }
 
+template <typename T, bool has_ff>
+static void rope_vision(const T * x, T * dst, const int ne0, const int ne1, const int ne2, const size_t s1,
+                        const size_t s2, const int n_dims, const int32_t * pos, const float freq_scale,
+                        const float ext_factor, const float attn_factor, const rope_corr_dims corr_dims,
+                        const float theta_scale, const float * freq_factors, const mrope_sections sections,
+                        const sycl::nd_item<3> & item_ct1) {
+    // get index pos
+    const int i0 = 2 * (item_ct1.get_group(1) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1));
+    if (i0 >= ne0) {
+        return;
+    }
+    const int    row_dst   = (item_ct1.get_group(2) * item_ct1.get_local_range(2)) + item_ct1.get_local_id(2);
+    const int    row_x     = row_dst % ne1;
+    const int    channel_x = row_dst / ne1;
+    const int    idst      = (row_dst * ne0) + (i0 / 2);
+    const size_t ix        = ((size_t) channel_x * s2) + ((size_t) row_x * s1) + (i0 / 2);
+
+    const int sect_dims = sections.v[0] + sections.v[1];
+    const int sector    = (i0 / 2) % sect_dims;
+
+    float theta_base = 0.0f;
+    if (sector < sections.v[0]) {
+        const int p = sector;
+        theta_base  = pos[channel_x] * sycl::pow(theta_scale, (float) p);
+    } else {
+        // Simplified from CUDA backend code: if (sector >= sections.v[0] && sector < sec_w) which is just sector >= sections.v[0]
+        const int p = sector - sections.v[0];
+        theta_base  = pos[channel_x + ne2] * sycl::pow(theta_scale, (float) p);
+    }
+
+    const float freq_factor = has_ff ? freq_factors[i0 / 2] : 1.0f;
+    float       cos_theta;
+    float       sin_theta;
+    rope_yarn(theta_base / freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
+    const float x0 = x[ix + 0];
+    const float x1 = x[ix + n_dims];
+
+    // store results in dst
+    dst[idst + 0]      = x0 * cos_theta - x1 * sin_theta;
+    dst[idst + n_dims] = x0 * sin_theta + x1 * cos_theta;
+}
+
 template <typename T>
 static void rope_norm_sycl(
     const T *x, T *dst, int ne0, int n_dims, int nr, const int32_t *pos, float freq_scale, int p_delta_rows,
@@ -192,21 +240,58 @@ static void rope_neox_sycl(
     }
 }
 
+// rope vision
+template <typename T>
+static void rope_vision_sycl(const T * x, T * dst, const int ne0, const int ne1, const int ne2, const size_t s1,
+                             const size_t s2, const int n_dims, const int nr, const int32_t * pos,
+                             const float freq_scale, const float freq_base, const float ext_factor,
+                             const float attn_factor, const rope_corr_dims corr_dims, const float * freq_factors,
+                             const mrope_sections sections, queue_ptr stream) {
+    GGML_ASSERT(ne0 % 2 == 0);
+    const sycl::range<3>    block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
+    const int               n_blocks_y = (ne0 + 2 * SYCL_ROPE_BLOCK_SIZE - 1) / (2 * SYCL_ROPE_BLOCK_SIZE);
+    const sycl::range<3>    grid_dims(1, n_blocks_y, nr);
+    const sycl::nd_range<3> nd_range(grid_dims * block_dims, block_dims);
+
+    const float theta_scale = std::pow(freq_base, -2.0f / n_dims);
+    // Add FP16 capability check if T could be sycl::half
+    if constexpr (std::is_same_v<T, sycl::half>) {
+        dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
+    }
+    // launch kernel
+    if (freq_factors == nullptr) {
+        stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) {
+            rope_vision<T, false>(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor,
+                                  corr_dims, theta_scale, freq_factors, sections, item_ct1);
+        });
+    } else {
+        stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) {
+            rope_vision<T, true>(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor,
+                                 corr_dims, theta_scale, freq_factors, sections, item_ct1);
+        });
+    }
+}
+
 void ggml_sycl_op_rope(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
 
     GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16);
     GGML_ASSERT( dst->type == GGML_TYPE_F32 ||  dst->type == GGML_TYPE_F16);
     GGML_ASSERT(dst->src[0]->type == dst->type);
-
-    const int64_t ne00 = dst->src[0]->ne[0];
-    const int64_t ne01 = dst->src[0]->ne[1];
+    const int64_t ne00 = dst->src[0]->ne[0]; // head dims
+    const int64_t ne01 = dst->src[0]->ne[1]; // num heads
+    const int64_t ne02 = dst->src[0]->ne[2]; // num heads
     const int64_t nr = ggml_nrows(dst->src[0]);
 
+    const size_t s01 = dst->src[0]->nb[1] / ggml_type_size(dst->src[0]->type);
+    const size_t s02 = dst->src[0]->nb[2] / ggml_type_size(dst->src[0]->type);
+
+
     //const int n_past      = ((int32_t *) dst->op_params)[0];
     const int n_dims      = ((int32_t *) dst->op_params)[1];
     const int mode        = ((int32_t *) dst->op_params)[2];
     //const int n_ctx       = ((int32_t *) dst->op_params)[3];
     const int n_ctx_orig  = ((int32_t *) dst->op_params)[4];
+    mrope_sections sections;
 
     // RoPE alteration for extended context
     float freq_base;
@@ -222,8 +307,10 @@ void ggml_sycl_op_rope(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
     memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
     memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
     memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
+    memcpy(&sections.v,  (int32_t *) dst->op_params + 11, sizeof(int)*4);
 
     const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
+    const bool is_vision = mode == GGML_ROPE_TYPE_VISION;
 
     const int32_t * pos = (const int32_t *) dst->src[1]->data;
 
@@ -240,6 +327,7 @@ void ggml_sycl_op_rope(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
 
     // compute
     if (is_neox) {
+        GGML_SYCL_DEBUG("%s: neox path\n", __func__);
         if (dst->src[0]->type == GGML_TYPE_F32) {
             rope_neox_sycl(
                 (const float *)dst->src[0]->data, (float *)dst->data, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
@@ -253,7 +341,19 @@ void ggml_sycl_op_rope(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
         } else {
             GGML_ABORT("fatal error");
         }
+    } else if (is_vision) {
+        GGML_SYCL_DEBUG("%s: vision path\n", __func__);
+        if (dst->src[0]->type == GGML_TYPE_F16) {
+            rope_vision_sycl((const sycl::half *)dst->src[0]->data, (sycl::half *)dst->data, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
+                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, main_stream);
+        } else if (dst->src[0]->type == GGML_TYPE_F32) {
+            rope_vision_sycl((const float *) dst->src[0]->data, (float *)dst->data, ne00, ne01, ne02, s01, s02, n_dims, nr, pos, freq_scale,
+                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, sections, main_stream);
+        } else {
+            GGML_ABORT("Fatal error: Tensor type unsupported!");
+        }
     } else {
+        GGML_SYCL_DEBUG("%s: norm path\n", __func__);
         if (dst->src[0]->type == GGML_TYPE_F32) {
             rope_norm_sycl(
                 (const float *)dst->src[0]->data, (float *)dst->data, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -5749,7 +5749,7 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
         }
         return nullptr;
     case GGML_OP_UPSCALE:
-        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 && dst->op_params[0] == GGML_SCALE_MODE_NEAREST) {
             return ctx->device->pipeline_upscale_f32;
         }
         return nullptr;
@@ -9404,9 +9404,10 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_COS:
         case GGML_OP_CLAMP:
             return op->src[0]->type == GGML_TYPE_F32;
+        case GGML_OP_UPSCALE:
+            return op->op_params[0] == GGML_SCALE_MODE_NEAREST;
         case GGML_OP_ACC:
         case GGML_OP_CONCAT:
-        case GGML_OP_UPSCALE:
         case GGML_OP_SCALE:
         case GGML_OP_PAD:
         case GGML_OP_DIAG_MASK_INF:
@@ -9774,7 +9775,7 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) {
     } else if (tensor->op == GGML_OP_CONCAT) {
         tensor_clone = ggml_concat(ggml_ctx, src_clone[0], src_clone[1], *(int *)tensor->op_params);
     } else if (tensor->op == GGML_OP_UPSCALE) {
-        tensor_clone = ggml_upscale_ext(ggml_ctx, src_clone[0], tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
+        tensor_clone = ggml_upscale_ext(ggml_ctx, src_clone[0], tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->op_params[0], tensor->op_params[1], (ggml_scale_mode) tensor->op_params[0]);
     } else if (tensor->op == GGML_OP_SCALE) {
         const float * params = (const float *)tensor->op_params;
         tensor_clone = ggml_scale(ggml_ctx, src_clone[0], params[0]);

ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp

@@ -201,6 +201,11 @@ void main() {
     uint32_t q_stride = p.gqa_ratio > 1 ? (p.nb02 / 4) : p.nb01;
     uint32_t k_stride = p.nb11;
     uint32_t v_stride = p.nb21;
+    // When using grouped query attention, all rows use the same mask (stride 0).
+    // "p.gqa_ratio >> 16" is just a roundabout way of writing zero
+    // that prevents the compiler from folding the "&" through the select
+    // and breaking the alignment detection.
+    uint32_t m_stride = (p.gqa_ratio > 1) ? (p.gqa_ratio >> 16) : KV;
     // hint to the compiler that strides are aligned for the aligned variant of the shader
     if (Clamp != gl_CooperativeMatrixClampModeConstantNV)
     {
@@ -209,6 +214,7 @@ void main() {
         k_stride &= ~7;
         v_stride &= ~7;
 #endif
+        m_stride &= ~7;
     }
     tensorLayoutQ = setTensorLayoutStrideNV(tensorLayoutQ, q_stride, 1);
     tensorLayoutK = setTensorLayoutStrideNV(tensorLayoutK, k_stride, 1);
@@ -261,10 +267,7 @@ void main() {
         if (p.mask != 0) {
             tensorLayoutNV<2, Clamp> tensorLayoutM = createTensorLayoutNV(2, Clamp);
             tensorLayoutM = setTensorLayoutDimensionNV(tensorLayoutM, p.nem1, KV);
-            // When using grouped query attention, all rows use the same mask.
-            if (p.gqa_ratio > 1) {
-                tensorLayoutM = setTensorLayoutStrideNV(tensorLayoutM, 0, 1);
-            }
+            tensorLayoutM = setTensorLayoutStrideNV(tensorLayoutM, m_stride, 1);
 
             coopmat<float16_t, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> mv;
 

ggml/src/ggml.c

@@ -982,23 +982,18 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
 
     "UNARY",
 
-    "MAP_UNARY",
-    "MAP_BINARY",
-
-    "MAP_CUSTOM1_F32",
-    "MAP_CUSTOM2_F32",
-    "MAP_CUSTOM3_F32",
-
     "MAP_CUSTOM1",
     "MAP_CUSTOM2",
     "MAP_CUSTOM3",
 
+    "CUSTOM",
+
     "CROSS_ENTROPY_LOSS",
     "CROSS_ENTROPY_LOSS_BACK",
     "OPT_STEP_ADAMW",
 };
 
-static_assert(GGML_OP_COUNT == 85, "GGML_OP_COUNT != 85");
+static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -1081,23 +1076,18 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
 
     "unary(x)",
 
-    "f(x)",
-    "f(x,y)",
-
-    "custom_f32(x)",
-    "custom_f32(x,y)",
-    "custom_f32(x,y,z)",
+    "map_custom(x)",
+    "map_custom(x,y)",
+    "map_custom(x,y,z)",
 
     "custom(x)",
-    "custom(x,y)",
-    "custom(x,y,z)",
 
     "cross_entropy_loss(x,y)",
     "cross_entropy_loss_back(x,y)",
     "adamw(x)",
 };
 
-static_assert(GGML_OP_COUNT == 85, "GGML_OP_COUNT != 85");
+static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81");
 
 static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
 
@@ -4184,7 +4174,8 @@ static struct ggml_tensor * ggml_upscale_impl(
         int                   ne0,
         int                   ne1,
         int                   ne2,
-        int                   ne3) {
+        int                   ne3,
+        enum ggml_scale_mode  mode) {
     GGML_ASSERT(a->ne[0] <= ne0);
     GGML_ASSERT(a->ne[1] <= ne1);
     GGML_ASSERT(a->ne[2] <= ne2);
@@ -4192,6 +4183,8 @@ static struct ggml_tensor * ggml_upscale_impl(
 
     struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3);
 
+    ggml_set_op_params_i32(result, 0, mode);
+
     result->op     = GGML_OP_UPSCALE;
     result->src[0] = a;
 
@@ -4201,8 +4194,9 @@ static struct ggml_tensor * ggml_upscale_impl(
 struct ggml_tensor * ggml_upscale(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
-        int                   scale_factor) {
-    return ggml_upscale_impl(ctx, a, a->ne[0] * scale_factor, a->ne[1] * scale_factor, a->ne[2], a->ne[3]);
+        int                   scale_factor,
+        enum ggml_scale_mode  mode) {
+    return ggml_upscale_impl(ctx, a, a->ne[0] * scale_factor, a->ne[1] * scale_factor, a->ne[2], a->ne[3], mode);
 }
 
 struct ggml_tensor * ggml_upscale_ext(
@@ -4211,8 +4205,9 @@ struct ggml_tensor * ggml_upscale_ext(
         int                   ne0,
         int                   ne1,
         int                   ne2,
-        int                   ne3) {
-    return ggml_upscale_impl(ctx, a, ne0, ne1, ne2, ne3);
+        int                   ne3,
+        enum ggml_scale_mode  mode) {
+    return ggml_upscale_impl(ctx, a, ne0, ne1, ne2, ne3, mode);
 }
 
 // ggml_pad
@@ -4842,179 +4837,6 @@ struct ggml_tensor * ggml_unary_inplace(
     return ggml_unary_impl(ctx, a, op, true);
 }
 
-// ggml_map_unary
-
-static struct ggml_tensor * ggml_map_unary_impl_f32(
-        struct ggml_context        * ctx,
-        struct ggml_tensor         * a,
-        const  ggml_unary_op_f32_t   fun,
-        bool                         inplace) {
-    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
-
-    ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
-
-    result->op     = GGML_OP_MAP_UNARY;
-    result->src[0] = a;
-
-    return result;
-}
-
-struct ggml_tensor * ggml_map_unary_f32(
-        struct ggml_context        * ctx,
-        struct ggml_tensor         * a,
-        const  ggml_unary_op_f32_t   fun) {
-    return ggml_map_unary_impl_f32(ctx, a, fun, false);
-}
-
-struct ggml_tensor * ggml_map_unary_inplace_f32(
-        struct ggml_context        * ctx,
-        struct ggml_tensor         * a,
-        const  ggml_unary_op_f32_t   fun) {
-    return ggml_map_unary_impl_f32(ctx, a, fun, true);
-}
-
-// ggml_map_binary
-
-static struct ggml_tensor * ggml_map_binary_impl_f32(
-        struct ggml_context         * ctx,
-        struct ggml_tensor          * a,
-        struct ggml_tensor          * b,
-        const  ggml_binary_op_f32_t   fun,
-        bool                          inplace) {
-    GGML_ASSERT(ggml_are_same_shape(a, b));
-
-    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
-
-    ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
-
-    result->op     = GGML_OP_MAP_BINARY;
-    result->src[0] = a;
-    result->src[1] = b;
-
-    return result;
-}
-
-struct ggml_tensor * ggml_map_binary_f32(
-        struct ggml_context         * ctx,
-        struct ggml_tensor          * a,
-        struct ggml_tensor          * b,
-        const  ggml_binary_op_f32_t   fun) {
-    return ggml_map_binary_impl_f32(ctx, a, b, fun, false);
-}
-
-struct ggml_tensor * ggml_map_binary_inplace_f32(
-        struct ggml_context         * ctx,
-        struct ggml_tensor          * a,
-        struct ggml_tensor          * b,
-        const  ggml_binary_op_f32_t   fun) {
-    return ggml_map_binary_impl_f32(ctx, a, b, fun, true);
-}
-
-// ggml_map_custom1_f32
-
-static struct ggml_tensor * ggml_map_custom1_impl_f32(
-        struct ggml_context          * ctx,
-        struct ggml_tensor           * a,
-        const  ggml_custom1_op_f32_t   fun,
-        bool                           inplace) {
-    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
-
-    ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
-
-    result->op     = GGML_OP_MAP_CUSTOM1_F32;
-    result->src[0] = a;
-
-    return result;
-}
-
-struct ggml_tensor * ggml_map_custom1_f32(
-        struct ggml_context          * ctx,
-        struct ggml_tensor           * a,
-        const  ggml_custom1_op_f32_t   fun) {
-    return ggml_map_custom1_impl_f32(ctx, a, fun, false);
-}
-
-struct ggml_tensor * ggml_map_custom1_inplace_f32(
-        struct ggml_context          * ctx,
-        struct ggml_tensor           * a,
-        const  ggml_custom1_op_f32_t   fun) {
-    return ggml_map_custom1_impl_f32(ctx, a, fun, true);
-}
-
-// ggml_map_custom2_f32
-
-static struct ggml_tensor * ggml_map_custom2_impl_f32(
-        struct ggml_context          * ctx,
-        struct ggml_tensor           * a,
-        struct ggml_tensor           * b,
-        const  ggml_custom2_op_f32_t   fun,
-        bool                           inplace) {
-    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
-
-    ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
-
-    result->op     = GGML_OP_MAP_CUSTOM2_F32;
-    result->src[0] = a;
-    result->src[1] = b;
-
-    return result;
-}
-
-struct ggml_tensor * ggml_map_custom2_f32(
-        struct ggml_context          * ctx,
-        struct ggml_tensor           * a,
-        struct ggml_tensor           * b,
-        const  ggml_custom2_op_f32_t   fun) {
-    return ggml_map_custom2_impl_f32(ctx, a, b, fun, false);
-}
-
-struct ggml_tensor * ggml_map_custom2_inplace_f32(
-        struct ggml_context          * ctx,
-        struct ggml_tensor           * a,
-        struct ggml_tensor           * b,
-        const  ggml_custom2_op_f32_t   fun) {
-    return ggml_map_custom2_impl_f32(ctx, a, b, fun, true);
-}
-
-// ggml_map_custom3_f32
-
-static struct ggml_tensor * ggml_map_custom3_impl_f32(
-        struct ggml_context          * ctx,
-        struct ggml_tensor           * a,
-        struct ggml_tensor           * b,
-        struct ggml_tensor           * c,
-        const  ggml_custom3_op_f32_t   fun,
-        bool                           inplace) {
-    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
-
-    ggml_set_op_params(result, (const void *) &fun, sizeof(fun));
-
-    result->op     = GGML_OP_MAP_CUSTOM3_F32;
-    result->src[0] = a;
-    result->src[1] = b;
-    result->src[2] = c;
-
-    return result;
-}
-
-struct ggml_tensor * ggml_map_custom3_f32(
-        struct ggml_context          * ctx,
-        struct ggml_tensor           * a,
-        struct ggml_tensor           * b,
-        struct ggml_tensor           * c,
-        const  ggml_custom3_op_f32_t   fun) {
-    return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, false);
-}
-
-struct ggml_tensor * ggml_map_custom3_inplace_f32(
-        struct ggml_context          * ctx,
-        struct ggml_tensor           * a,
-        struct ggml_tensor           * b,
-        struct ggml_tensor           * c,
-        const  ggml_custom3_op_f32_t   fun) {
-    return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, true);
-}
-
 // ggml_map_custom1
 
 static struct ggml_tensor * ggml_map_custom1_impl(
@@ -5033,7 +4855,7 @@ static struct ggml_tensor * ggml_map_custom1_impl(
         /*.n_tasks  =*/ n_tasks,
         /*.userdata =*/ userdata
     };
-    ggml_set_op_params(result, (const void *) &params, sizeof(params));
+    ggml_set_op_params(result, &params, sizeof(params));
 
     result->op     = GGML_OP_MAP_CUSTOM1;
     result->src[0] = a;
@@ -5078,7 +4900,7 @@ static struct ggml_tensor * ggml_map_custom2_impl(
         /*.n_tasks  =*/ n_tasks,
         /*.userdata =*/ userdata
     };
-    ggml_set_op_params(result, (const void *) &params, sizeof(params));
+    ggml_set_op_params(result, &params, sizeof(params));
 
     result->op     = GGML_OP_MAP_CUSTOM2;
     result->src[0] = a;
@@ -5127,7 +4949,7 @@ static struct ggml_tensor * ggml_map_custom3_impl(
         /*.n_tasks  =*/ n_tasks,
         /*.userdata =*/ userdata
     };
-    ggml_set_op_params(result, (const void *) &params, sizeof(params));
+    ggml_set_op_params(result, &params, sizeof(params));
 
     result->op     = GGML_OP_MAP_CUSTOM3;
     result->src[0] = a;
@@ -5159,6 +4981,66 @@ struct ggml_tensor * ggml_map_custom3_inplace(
     return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, true);
 }
 
+struct ggml_tensor * ggml_custom_4d(
+        struct ggml_context * ctx,
+        enum ggml_type        type,
+        int64_t               ne0,
+        int64_t               ne1,
+        int64_t               ne2,
+        int64_t               ne3,
+        struct ggml_tensor ** args,
+        int                   n_args,
+        ggml_custom_op_t      fun,
+        int                   n_tasks,
+        void                * userdata) {
+
+    GGML_ASSERT(n_args < GGML_MAX_SRC);
+
+    struct ggml_tensor * result = ggml_new_tensor_4d(ctx, type, ne0, ne1, ne2, ne3);
+
+    struct ggml_custom_op_params params = {
+        /*.fun      =*/ fun,
+        /*.n_tasks  =*/ n_tasks,
+        /*.userdata =*/ userdata
+    };
+    ggml_set_op_params(result, &params, sizeof(params));
+
+    result->op = GGML_OP_CUSTOM;
+    for (int i = 0; i < n_args; i++) {
+        result->src[i] = args[i];
+    }
+
+    return result;
+}
+
+struct ggml_tensor * ggml_custom_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor ** args,
+        int                   n_args,
+        ggml_custom_op_t      fun,
+        int                   n_tasks,
+        void                * userdata) {
+
+    GGML_ASSERT(n_args < GGML_MAX_SRC - 1);
+
+    struct ggml_tensor * result = ggml_view_tensor(ctx, a);
+
+    struct ggml_custom_op_params params = {
+        /*.fun      =*/ fun,
+        /*.n_tasks  =*/ n_tasks,
+        /*.userdata =*/ userdata
+    };
+    ggml_set_op_params(result, &params, sizeof(params));
+
+    result->op = GGML_OP_CUSTOM;
+    result->src[0] = a;
+    for (int i = 0; i < n_args; i++) {
+        result->src[i + 1] = args[i];
+    }
+
+    return result;
+}
 // ggml_cross_entropy_loss
 
 struct ggml_tensor * ggml_cross_entropy_loss(

gguf-py/gguf/constants.py

@@ -139,6 +139,8 @@ class Keys:
         REL_BUCKETS_COUNT            = "{arch}.attention.relative_buckets_count"
         SLIDING_WINDOW               = "{arch}.attention.sliding_window"
         SCALE                        = "{arch}.attention.scale"
+        KEY_LENGTH_MLA               = "{arch}.attention.key_length_mla"
+        VALUE_LENGTH_MLA             = "{arch}.attention.value_length_mla"
 
     class Rope:
         DIMENSION_COUNT         = "{arch}.rope.dimension_count"
@@ -280,6 +282,7 @@ class MODEL_ARCH(IntEnum):
     DEEPSEEK         = auto()
     DEEPSEEK2        = auto()
     CHATGLM          = auto()
+    GLM4             = auto()
     BITNET           = auto()
     T5               = auto()
     T5ENCODER        = auto()
@@ -381,6 +384,8 @@ class MODEL_TENSOR(IntEnum):
     ATTN_Q_B             = auto()
     ATTN_KV_A_MQA        = auto()
     ATTN_KV_B            = auto()
+    ATTN_K_B             = auto()
+    ATTN_V_B             = auto()
     ATTN_Q_A_NORM        = auto()
     ATTN_KV_A_NORM       = auto()
     FFN_SUB_NORM         = auto()
@@ -487,6 +492,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.DEEPSEEK:         "deepseek",
     MODEL_ARCH.DEEPSEEK2:        "deepseek2",
     MODEL_ARCH.CHATGLM:          "chatglm",
+    MODEL_ARCH.GLM4:             "glm4",
     MODEL_ARCH.BITNET:           "bitnet",
     MODEL_ARCH.T5:               "t5",
     MODEL_ARCH.T5ENCODER:        "t5encoder",
@@ -588,6 +594,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.ATTN_Q_B:                  "blk.{bid}.attn_q_b",
     MODEL_TENSOR.ATTN_KV_A_MQA:             "blk.{bid}.attn_kv_a_mqa",
     MODEL_TENSOR.ATTN_KV_B:                 "blk.{bid}.attn_kv_b",
+    MODEL_TENSOR.ATTN_K_B:                  "blk.{bid}.attn_k_b",
+    MODEL_TENSOR.ATTN_V_B:                  "blk.{bid}.attn_v_b",
     MODEL_TENSOR.ATTN_Q_A_NORM:             "blk.{bid}.attn_q_a_norm",
     MODEL_TENSOR.ATTN_KV_A_NORM:            "blk.{bid}.attn_kv_a_norm",
     MODEL_TENSOR.ATTN_SUB_NORM:             "blk.{bid}.attn_sub_norm",
@@ -1515,6 +1523,8 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.ATTN_Q_B,
         MODEL_TENSOR.ATTN_KV_A_MQA,
         MODEL_TENSOR.ATTN_KV_B,
+        MODEL_TENSOR.ATTN_K_B,
+        MODEL_TENSOR.ATTN_V_B,
         MODEL_TENSOR.ATTN_Q_A_NORM,
         MODEL_TENSOR.ATTN_KV_A_NORM,
         MODEL_TENSOR.ATTN_OUT,
@@ -1561,6 +1571,23 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.GLM4 : [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.FFN_POST_NORM,
+    ],
     MODEL_ARCH.BITNET: [
         MODEL_TENSOR.ATTN_Q,
         MODEL_TENSOR.ATTN_K,

gguf-py/gguf/gguf_writer.py

@@ -689,6 +689,12 @@ class GGUFWriter:
     def add_value_length(self, length: int) -> None:
         self.add_uint32(Keys.Attention.VALUE_LENGTH.format(arch=self.arch), length)
 
+    def add_key_length_mla(self, length: int) -> None:
+        self.add_uint32(Keys.Attention.KEY_LENGTH_MLA.format(arch=self.arch), length)
+
+    def add_value_length_mla(self, length: int) -> None:
+        self.add_uint32(Keys.Attention.VALUE_LENGTH_MLA.format(arch=self.arch), length)
+
     def add_max_alibi_bias(self, bias: float) -> None:
         self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias)
 

gguf-py/gguf/tensor_mapping.py

@@ -13,7 +13,7 @@ class TensorNameMap:
             "transformer.wte",                           # gpt2 gpt-j mpt refact qwen dbrx jais exaone
             "transformer.word_embeddings",               # falcon
             "word_embeddings",                           # bloom
-            "model.embed_tokens",                        # llama-hf nemotron olmoe olmo2 rwkv6qwen2
+            "model.embed_tokens",                        # llama-hf nemotron olmoe olmo2 rwkv6qwen2 glm4-0414
             "tok_embeddings",                            # llama-pth
             "embeddings.word_embeddings",                # bert nomic-bert
             "language_model.embedding.word_embeddings",  # persimmon
@@ -30,6 +30,7 @@ class TensorNameMap:
             "rwkv.embeddings",                           # rwkv6
             "model.embeddings",                          # rwkv7
             "model.word_embeddings",                     # bailingmoe
+            "language_model.model.embed_tokens",         # llama4
         ),
 
         # Token type embeddings
@@ -67,6 +68,7 @@ class TensorNameMap:
             "output_layer",              # chatglm
             "head",                      # rwkv
             "head.out",                  # wavtokenizer
+            "language_model.lm_head",    # llama4
         ),
 
         # Output norm
@@ -89,6 +91,7 @@ class TensorNameMap:
             "rwkv.ln_out",                             # rwkv6
             "model.ln_out",                            # rwkv7
             "backbone.final_layer_norm",               # wavtokenizer
+            "language_model.model.norm",               # llama4
         ),
 
         # Rope frequencies
@@ -130,6 +133,7 @@ class TensorNameMap:
             "transformer.layers.{bid}.attn_norm",                   # openelm
             "rwkv.blocks.{bid}.ln1",                                # rwkv6
             "model.layers.{bid}.ln1",                               # rwkv7
+            "language_model.model.layers.{bid}.input_layernorm",    # llama4
         ),
 
         # Attention norm 2
@@ -169,6 +173,7 @@ class TensorNameMap:
             "model.layers.{bid}.attention.wq",                           # internlm2
             "transformer.decoder_layer.{bid}.multi_head_attention.query",# Grok
             "transformer.h.{bid}.attn.attention.q_proj",                 # exaone
+            "language_model.model.layers.{bid}.self_attn.q_proj",        # llama4
         ),
 
         # Attention key
@@ -183,6 +188,7 @@ class TensorNameMap:
             "model.layers.{bid}.attention.wk",                         # internlm2
             "transformer.decoder_layer.{bid}.multi_head_attention.key",# Grok
             "transformer.h.{bid}.attn.attention.k_proj",               # exaone
+            "language_model.model.layers.{bid}.self_attn.k_proj",      # llama4
         ),
 
         # Attention value
@@ -196,6 +202,7 @@ class TensorNameMap:
             "model.layers.{bid}.attention.wv",                           # internlm2
             "transformer.decoder_layer.{bid}.multi_head_attention.value",# Grok
             "transformer.h.{bid}.attn.attention.v_proj",                 # exaone
+            "language_model.model.layers.{bid}.self_attn.v_proj",        # llama4
         ),
 
         # Attention output
@@ -222,6 +229,7 @@ class TensorNameMap:
             "encoder.layers.{bid}.self_attention.dense",                    # chatglm
             "transformer.layers.{bid}.attn.out_proj",                       # openelm
             "transformer.h.{bid}.attn.attention.out_proj",                  # exaone
+            "language_model.model.layers.{bid}.self_attn.o_proj",           # llama4
         ),
 
         # Attention output norm
@@ -233,7 +241,8 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.ATTN_POST_NORM: (
-            "model.layers.{bid}.post_attention_layernorm",     # gemma2 olmo2
+            "model.layers.{bid}.post_attention_layernorm",     # gemma2 olmo2    # ge
+            "model.layers.{bid}.post_self_attn_layernorm",     # glm-4-0414
         ),
 
         # Rotary embeddings
@@ -259,6 +268,7 @@ class TensorNameMap:
             "transformer.decoder_layer.{bid}.rms_norm_2",                    # Grok
             "encoder.layers.{bid}.post_attention_layernorm",                 # chatglm
             "transformer.layers.{bid}.ffn_norm",                             # openelm
+            "language_model.model.layers.{bid}.post_attention_layernorm",    # llama4
         ),
 
         # Post feed-forward norm
@@ -269,6 +279,7 @@ class TensorNameMap:
         # Post feed-forward norm
         MODEL_TENSOR.FFN_POST_NORM: (
             "model.layers.{bid}.post_feedforward_layernorm", # gemma2 olmo2
+            "model.layers.{bid}.post_mlp_layernorm", # glm-4-0414
         ),
 
         MODEL_TENSOR.FFN_GATE_INP: (
@@ -278,6 +289,7 @@ class TensorNameMap:
             "transformer.decoder_layer.{bid}.router",           # Grok
             "transformer.blocks.{bid}.ffn.router.layer",        # dbrx
             "model.layers.{bid}.block_sparse_moe.router.layer", # granitemoe
+            "language_model.model.layers.{bid}.feed_forward.router", # llama4
         ),
 
         MODEL_TENSOR.FFN_GATE_INP_SHEXP: (
@@ -306,7 +318,7 @@ class TensorNameMap:
             "h.{bid}.mlp.c_fc",                                       # gpt2
             "transformer.h.{bid}.mlp.fc1",                            # phi2
             "model.layers.{bid}.mlp.fc1",                             # phi2
-            "model.layers.{bid}.mlp.gate_up_proj",                    # phi3
+            "model.layers.{bid}.mlp.gate_up_proj",                    # phi3 glm-4-0414
             "model.layers.layers.{bid}.mlp.up_proj",                  # plamo
             "model.layers.{bid}.feed_forward.w3",                     # internlm2
             "encoder.layers.{bid}.mlp.fc11",                          # nomic-bert
@@ -315,6 +327,7 @@ class TensorNameMap:
             "model.layers.{bid}.residual_mlp.w3",                     # arctic
             "encoder.layers.{bid}.mlp.dense_h_to_4h",                 # chatglm
             "transformer.h.{bid}.mlp.c_fc_1",                         # exaone
+            "language_model.model.layers.{bid}.feed_forward.up_proj", # llama4
         ),
 
         MODEL_TENSOR.FFN_UP_EXP: (
@@ -323,11 +336,13 @@ class TensorNameMap:
             "transformer.blocks.{bid}.ffn.experts.mlp.v1",    # dbrx
             "model.layers.{bid}.mlp.experts.up_proj",         # qwen2moe olmoe (merged)
             "model.layers.{bid}.block_sparse_moe.experts.w3", # phimoe (merged)
+            "language_model.model.layers.{bid}.feed_forward.experts.up_proj", # llama4
         ),
 
         MODEL_TENSOR.FFN_UP_SHEXP: (
             "model.layers.{bid}.mlp.shared_expert.up_proj",  # qwen2moe
             "model.layers.{bid}.mlp.shared_experts.up_proj", # deepseek deepseek2
+            "language_model.model.layers.{bid}.feed_forward.shared_expert.up_proj", # llama4
         ),
 
         # AWQ-activation gate
@@ -348,6 +363,7 @@ class TensorNameMap:
             "transformer.h.{bid}.mlp.linear_1",           # refact
             "model.layers.{bid}.residual_mlp.w1",         # arctic
             "transformer.h.{bid}.mlp.c_fc_0",             # exaone
+            "language_model.model.layers.{bid}.feed_forward.gate_proj", # llama4
         ),
 
         MODEL_TENSOR.FFN_GATE_EXP: (
@@ -356,11 +372,13 @@ class TensorNameMap:
             "transformer.blocks.{bid}.ffn.experts.mlp.w1",    # dbrx
             "model.layers.{bid}.mlp.experts.gate_proj",       # qwen2moe olmoe (merged)
             "model.layers.{bid}.block_sparse_moe.experts.w1", # phimoe (merged)
+            "language_model.model.layers.{bid}.feed_forward.experts.gate_proj", # llama4
         ),
 
         MODEL_TENSOR.FFN_GATE_SHEXP: (
             "model.layers.{bid}.mlp.shared_expert.gate_proj",  # qwen2moe
             "model.layers.{bid}.mlp.shared_experts.gate_proj", # deepseek deepseek2
+            "language_model.model.layers.{bid}.feed_forward.shared_expert.gate_proj", # llama4
         ),
 
         # Feed-forward down
@@ -389,6 +407,7 @@ class TensorNameMap:
             "encoder.layer.{bid}.mlp.down_layer",                     # jina-bert-v2
             "encoder.layers.{bid}.mlp.dense_4h_to_h",                 # chatglm
             "model.layers.h.{bid}.mlp.c_proj",                        # exaone
+            "language_model.model.layers.{bid}.feed_forward.down_proj", # llama4
         ),
 
         MODEL_TENSOR.FFN_DOWN_EXP: (
@@ -398,11 +417,13 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.experts.down_proj",          # qwen2moe olmoe (merged)
             "model.layers.{bid}.block_sparse_moe.output_linear", # granitemoe
             "model.layers.{bid}.block_sparse_moe.experts.w2",    # phimoe (merged)
+            "language_model.model.layers.{bid}.feed_forward.experts.down_proj", # llama4
         ),
 
         MODEL_TENSOR.FFN_DOWN_SHEXP: (
             "model.layers.{bid}.mlp.shared_expert.down_proj",  # qwen2moe
             "model.layers.{bid}.mlp.shared_experts.down_proj", # deepseek deepseek2
+            "language_model.model.layers.{bid}.feed_forward.shared_expert.down_proj", # llama4
         ),
 
         MODEL_TENSOR.ATTN_Q_NORM: (
@@ -656,6 +677,14 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.kv_b_proj", # deepseek2
         ),
 
+        MODEL_TENSOR.ATTN_K_B: (
+            "model.layers.{bid}.self_attn.k_b_proj",  # deepseek2
+        ),
+
+        MODEL_TENSOR.ATTN_V_B: (
+            "model.layers.{bid}.self_attn.v_b_proj",  # deepseek2
+        ),
+
         MODEL_TENSOR.ATTN_Q_A_NORM: (
             "model.layers.{bid}.self_attn.q_a_layernorm", # deepseek2
         ),

gguf-py/gguf/utility.py

@@ -1,7 +1,11 @@
 from __future__ import annotations
 
+from dataclasses import dataclass
 from typing import Literal
 
+import os
+import json
+
 
 def fill_templated_filename(filename: str, output_type: str | None) -> str:
     # Given a file name fill in any type templates e.g. 'some-model-name.{ftype}.gguf'
@@ -67,3 +71,194 @@ def naming_convention(model_name: str | None, base_name: str | None, finetune_st
     kind = f"-{model_type.strip().replace(' ', '-')}" if model_type is not None else ""
 
     return f"{name}{parameters}{finetune}{version}{encoding}{kind}"
+
+
+@dataclass
+class RemoteTensor:
+    dtype: str
+    shape: tuple[int, ...]
+    offset_start: int
+    size: int
+    url: str
+
+    def data(self) -> bytearray:
+        # TODO: handle request errors (maybe with limited retries?)
+        # NOTE: using a bytearray, otherwise PyTorch complains the buffer is not writeable
+        data = bytearray(SafetensorRemote.get_data_by_range(url=self.url, start=self.offset_start, size=self.size))
+        return data
+
+
+class SafetensorRemote:
+    """
+    Uility class to handle remote safetensor files.
+    This class is designed to work with Hugging Face model repositories.
+
+    Example (one model has single safetensor file, the other has multiple):
+        for model_id in ["ngxson/TEST-Tiny-Llama4", "Qwen/Qwen2.5-7B-Instruct"]:
+            tensors = SafetensorRemote.get_list_tensors_hf_model(model_id)
+            print(tensors)
+
+    Example reading tensor data:
+        tensors = SafetensorRemote.get_list_tensors_hf_model(model_id)
+        for name, meta in tensors.items():
+            dtype, shape, offset_start, size, remote_safetensor_url = meta
+            # read the tensor data
+            data = SafetensorRemote.get_data_by_range(remote_safetensor_url, offset_start, size)
+            print(data)
+    """
+
+    BASE_DOMAIN = "https://huggingface.co"
+    ALIGNMENT = 8 # bytes
+
+    @classmethod
+    def get_list_tensors_hf_model(cls, model_id: str) -> dict[str, RemoteTensor]:
+        """
+        Get list of tensors from a Hugging Face model repository.
+
+        Returns a dictionary of tensor names and their metadata.
+        Each tensor is represented as a tuple of (dtype, shape, offset_start, size, remote_safetensor_url)
+        """
+        # case 1: model has only one single model.safetensor file
+        is_single_file = cls.check_file_exist(f"{cls.BASE_DOMAIN}/{model_id}/resolve/main/model.safetensors")
+        if is_single_file:
+            url = f"{cls.BASE_DOMAIN}/{model_id}/resolve/main/model.safetensors"
+            return cls.get_list_tensors(url)
+
+        # case 2: model has multiple files
+        index_url = f"{cls.BASE_DOMAIN}/{model_id}/resolve/main/model.safetensors.index.json"
+        is_multiple_files = cls.check_file_exist(index_url)
+        if is_multiple_files:
+            # read the index file
+            index_data = cls.get_data_by_range(index_url, 0)
+            index_str = index_data.decode('utf-8')
+            index_json = json.loads(index_str)
+            assert index_json.get("weight_map") is not None, "weight_map not found in index file"
+            weight_map = index_json["weight_map"]
+            # get the list of files
+            all_files = list(set(weight_map.values()))
+            all_files.sort() # make sure we load shard files in order
+            # get the list of tensors
+            tensors: dict[str, RemoteTensor] = {}
+            for file in all_files:
+                url = f"{cls.BASE_DOMAIN}/{model_id}/resolve/main/{file}"
+                for key, val in cls.get_list_tensors(url).items():
+                    tensors[key] = val
+            return tensors
+
+        raise ValueError(f"Model {model_id} does not have any safetensor files")
+
+    @classmethod
+    def get_list_tensors(cls, url: str) -> dict[str, RemoteTensor]:
+        """
+        Get list of tensors from a remote safetensor file.
+
+        Returns a dictionary of tensor names and their metadata.
+        Each tensor is represented as a tuple of (dtype, shape, offset_start, size)
+        """
+        metadata, data_start_offset = cls.get_metadata(url)
+        res: dict[str, RemoteTensor] = {}
+
+        for name, meta in metadata.items():
+            if name == "__metadata__":
+                continue
+            if not isinstance(meta, dict):
+                raise ValueError(f"Invalid metadata for tensor '{name}': {meta}")
+            try:
+                dtype = meta["dtype"]
+                shape = meta["shape"]
+                offset_start_relative, offset_end_relative = meta["data_offsets"]
+                size = offset_end_relative - offset_start_relative
+                offset_start = data_start_offset + offset_start_relative
+                res[name] = RemoteTensor(dtype=dtype, shape=tuple(shape), offset_start=offset_start, size=size, url=url)
+            except KeyError as e:
+                raise ValueError(f"Missing key in metadata for tensor '{name}': {e}, meta = {meta}")
+
+        return res
+
+    @classmethod
+    def get_metadata(cls, url: str) -> tuple[dict, int]:
+        """
+        Get JSON metadata from a remote safetensor file.
+
+        Returns tuple of (metadata, data_start_offset)
+        """
+        # Request first 5MB of the file (hopefully enough for metadata)
+        read_size = 5 * 1024 * 1024
+        raw_data = cls.get_data_by_range(url, 0, read_size)
+
+        # Parse header
+        # First 8 bytes contain the metadata length as u64 little-endian
+        if len(raw_data) < 8:
+            raise ValueError("Not enough data to read metadata size")
+        metadata_length = int.from_bytes(raw_data[:8], byteorder='little')
+
+        # Calculate the data start offset
+        data_start_offset = 8 + metadata_length
+        alignment = SafetensorRemote.ALIGNMENT
+        if data_start_offset % alignment != 0:
+            data_start_offset += alignment - (data_start_offset % alignment)
+
+        # Check if we have enough data to read the metadata
+        if len(raw_data) < 8 + metadata_length:
+            raise ValueError(f"Could not read complete metadata. Need {8 + metadata_length} bytes, got {len(raw_data)}")
+
+        # Extract metadata bytes and parse as JSON
+        metadata_bytes = raw_data[8:8 + metadata_length]
+        metadata_str = metadata_bytes.decode('utf-8')
+        try:
+            metadata = json.loads(metadata_str)
+            return metadata, data_start_offset
+        except json.JSONDecodeError as e:
+            raise ValueError(f"Failed to parse safetensor metadata as JSON: {e}")
+
+    @classmethod
+    def get_data_by_range(cls, url: str, start: int, size: int = -1) -> bytes:
+        """
+        Get raw byte data from a remote file by range.
+        If size is not specified, it will read the entire file.
+        """
+        import requests
+        from urllib.parse import urlparse
+
+        parsed_url = urlparse(url)
+        if not parsed_url.scheme or not parsed_url.netloc:
+            raise ValueError(f"Invalid URL: {url}")
+
+        headers = cls._get_request_headers()
+        if size > -1:
+            headers["Range"] = f"bytes={start}-{start + size}"
+        response = requests.get(url, allow_redirects=True, headers=headers)
+        response.raise_for_status()
+
+        # Get raw byte data
+        return response.content[:size]
+
+    @classmethod
+    def check_file_exist(cls, url: str) -> bool:
+        """
+        Check if a file exists at the given URL.
+        Returns True if the file exists, False otherwise.
+        """
+        import requests
+        from urllib.parse import urlparse
+
+        parsed_url = urlparse(url)
+        if not parsed_url.scheme or not parsed_url.netloc:
+            raise ValueError(f"Invalid URL: {url}")
+
+        try:
+            headers = cls._get_request_headers()
+            headers["Range"] = "bytes=0-0"
+            response = requests.head(url, allow_redirects=True, headers=headers)
+            # Success (2xx) or redirect (3xx)
+            return 200 <= response.status_code < 400
+        except requests.RequestException:
+            return False
+
+    @classmethod
+    def _get_request_headers(cls) -> dict[str, str]:
+        """Prepare common headers for requests."""
+        headers = {"User-Agent": "convert_hf_to_gguf"}
+        if os.environ.get("HF_TOKEN"):
+            headers["Authorization"] = f"Bearer {os.environ['HF_TOKEN']}"
+        return headers

include/llama.h

@@ -367,17 +367,18 @@ extern "C" {
 
     // model quantization parameters
     typedef struct llama_model_quantize_params {
-        int32_t nthread;                     // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
-        enum llama_ftype ftype;              // quantize to this llama_ftype
-        enum ggml_type output_tensor_type;   // output tensor type
-        enum ggml_type token_embedding_type; // token embeddings tensor type
-        bool allow_requantize;               // allow quantizing non-f32/f16 tensors
-        bool quantize_output_tensor;         // quantize output.weight
-        bool only_copy;                      // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
-        bool pure;                           // quantize all tensors to the default type
-        bool keep_split;                     // quantize to the same number of shards
-        void * imatrix;                      // pointer to importance matrix data
-        void * kv_overrides;                 // pointer to vector containing overrides
+        int32_t nthread;                      // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
+        enum llama_ftype ftype;               // quantize to this llama_ftype
+        enum ggml_type output_tensor_type;    // output tensor type
+        enum ggml_type token_embedding_type;  // token embeddings tensor type
+        bool allow_requantize;                // allow quantizing non-f32/f16 tensors
+        bool quantize_output_tensor;          // quantize output.weight
+        bool only_copy;                       // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
+        bool pure;                            // quantize all tensors to the default type
+        bool keep_split;                      // quantize to the same number of shards
+        void * imatrix;                       // pointer to importance matrix data
+        void * kv_overrides;                  // pointer to vector containing overrides
+        void * tensor_types;                  // pointer to vector containing tensor types
     } llama_model_quantize_params;
 
     typedef struct llama_logit_bias {